Impacts of Non-Native Species in a Shallow Lake: a Simulation Modeling Approach for Restoration and Management Michael Eric Colvin Iowa State University

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2012 Impacts of non-native species in a shallow lake: a simulation modeling approach for restoration and management Michael Eric Colvin Iowa State University

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Impacts of non-native species in a shallow lake: a simulation modeling approach for restoration and management

Michael Eric Colvin

A dissertation submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Major: Fisheries Biology

Program of Study Committee: Clay Pierce, Co-Major Professor Timothy Stewart, Co-Major Professor Philip Dixon David Otis Kevin de Laplante

Iowa State University

Ames, Iowa

2012

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION 1

CHAPTER 2. SEMI-DISCRETE BIOMASS DYNAMIC MODELING: AN 21 IMPROVED APPROACH FOR ASSESSING FISH STOCK RESPONSES TO PULSED HARVEST EVENTS

CHAPTER 3. STRATEGIES TO CONTROL A COMMON CARP (CYPRINUS 55 CARPIO) POPULATION BY PULSED COMMERCIAL HARVEST

CHAPTER 4. COMMON CARP, ZEBRA MUSSELS, AND THE FOOD WEB: 101 CONSEQUENCES OF NON-NATIVE SPECIES INVASIONS FOR LAKE RESTORATION AND RECREATIONAL FISHERIES

CHAPTER 5. A SIMULATION APPROACH TO EVALUATE POTENTIAL 155 NONNATIVE SPECIES IMPACTS ON WATER QUALITY AND FISHERY YIELD IN A SHALLOW EUTROPHIC LAKE UNDERGOING RESTORATION 200 CHAPTER 6. GENERAL CONCLUSIONS

202 APPENDIX 1. BIOTIC AND ABIOTIC SAMPLING

213 APPENDIX 2. ECOPATH BASIC INPUTS

235 APPENDIX 3. LAKE CHARACTERISTICS MODULE

240 APPENDIX 4. FOOD WEB MODULE

APPENDIX 5. FISHERY MODULE 261

APPENDIX 6. NUTRIENTS MODULE 262

APPENDIX 7. SUSPENDED SOLIDS MODULE 267

APPENDIX 8. WATER QUALITY MODULE 271

APPENDIX 9. PREDICTING TURBIDITY FROM TOTAL SUSPENDED SOLIDS 272

APPENDIX 10. PREDICTING SECCHI TRANSPARENCY 274

APPENDIX 11. SIMULATION OUTPUT 275

iii

ACKNOWLEDGEMENTS

I am grateful for the support and opportunity provided by my advisors Drs. Clay

Pierce and Tim Stewart. Additionally, I acknowledge the contributions of my committee

members, Dr. Kevin deLaplante, Dr. Philip Dixon, and Dr. David Otis to my academic

development and this dissertation. I am grateful for the efforts of Iowa Department of

Natural Resources personnel: J. Wahl, S. Gummer, J. Christenson, E. Thelen, D. Fjelt, J.

Larschied, K. Bogenshutz, J Euchner. Lisa Fascher provided assistance in field work and

water quality data that we would like to acknowledge. I am grateful to Iowa State University

personnel for assisting in field work including V. Hengtes, Z. Jackson, J. Koch, T. Neebling,

J. Lore, J. Schmitz, C. Penne, D. Wooley, J. Norlin, G. Scholten, E. Kiefer, M. Sundberg, J.

Skiff, and K. Rebarchek. I am grateful to David Knoll for support, discussion, and level of

involvement and assistance with the project. I am grateful to Jesse Fischer, Bryan Bakevich,

Tim Parks, Zak Jackson, Jeff Koch, Maria Dzul for discussions and thoughtful insights to this project. This project would not have been possible without assistance and support from the faculty, staff, and students of the Department of Natural Resource Ecology and

Management at Iowa State University and the Iowa Cooperative Fish and Wildlife Research

Unit. This project was supported in part by the Department of Natural Resource Ecology and

Management at Iowa State University, Iowa Cooperative Fish and Wildlife Research Unit, and Iowa Department of Natural Resources. Lastly, I acknowledge the constant support I received from my wife, Megan. I acknowledge support a great deal of support from my family, Owen and Hazel.

CHAPTER 1: INTRODUCTION

Water quality in lakes of agricultural landscapes

Declining water quality is a national problem. A recent nationwide water quality

assessment (EPA 2000) reports that 45% of lakes in the USA are impaired for one or more

designated uses. Nutrients and suspended sediment are among the most prominent

pollutants, with nutrients affecting 45% and suspended sediments affecting 10% of impaired

lakes. Agriculture is identified as the primary source of lake impairment, affecting 30% of

the impaired lakes. Research directed at improving water quality in lakes will help solve one

of North America’s most difficult and pervasive environmental problems.

Water quality problems are especially severe in Iowa. Iowa leads the USA in percent

of land area converted to cropland at 72% (NRCS 2000). Combined with pastureland (10%)

and developed land (5%), 87% of Iowa’s land area is impacted by either agriculture or

urbanization. Thirty five percent of Iowa’s lakes are now impaired for use by aquatic life, with sediment and nutrients being major contributors to water quality degradation (EPA

2000). Arbuckle and Downing (2001) reported that nutrient levels in Iowa lakes are among the highest in the world. Burkart et al. (2004) reported higher phosphorus concentrations in groundwater than proposed standards for surface waters at several Iowa sites, suggesting that even if point- and nonpoint-sources of nutrients are reduced through conservation measures, nutrient inputs might remain sufficiently high in some areas to prevent improvement in surface water quality. Research directed at improving water quality in lakes will help solve one of Iowa’s most difficult and pervasive environmental problems.

Biotic factors affecting water quality: an overview 2

There is growing recognition that internal components of ecosystems, including biotic factors, also have significant effects on water quality in lakes (Egertson and Downing 2004;

Bierman et al. 2005; Chumchal et al. 2005; Zimmer et al. 2006). There are several hypothesized models of internal loading of nutrients due to biotic factors. Fish can facilitate higher algal abundance due to nutrient excretion (Brabrand et al. 1990; Schindler and Eby

1997). Predation by fish can mediate changes in zooplankton assemblage structure which in turn can modify zooplankton effect on nutrient cycling and stimulate phytoplankton (Sterner et al. 1992). Phytoplankton abundance can also increase due to fish predation, reducing the number of large bodied zooplankton that are effective grazers of phytoplankton blooms

(Carpenter et al. 1985). Fish can have a significant effect on nutrient cycling in freshwater ecosystems (Zimmer et al. 2006). Internal nutrient cycling is not only influenced by fish.

Benthic invertebrates such as zebra mussels can filter large quantities of phytoplankton, acting as a biofilter (Pires et al. 2005). The excretion of nutrients by zebra mussels has also been coupled with blooms of cyanobacteria (James et al. 2000).

Biomanipulation, defined as change in biological structure by removing and/or stocking organisms, is increasingly being used to regulate water quality and other lake characteristics (Gulati et al. 1990; Beklioglu 2003). Early applications of biomanipulation were aimed primarily at “top-down” effects, whereby increased populations of large predaceous fish reduced planktivorous fish abundance, which consequently increased zooplankton abundance, decreased phytoplankton abundance, and improved water quality

(Shapiro et al. 1975; McQueen et al. 1986). Recently there has been increased recognition of potential for “bottom-up” biomanipulation effects, where reductions in biomass of certain fish species or the introduction of filter-feeding invertebrates results in water quality 3

improvement through reduced nutrient excretion and phytoplankton grazing (e.g., Horppila et

al. 1998). In small shallow lakes of Europe, zebra mussels have been introduced as a

biomanipulation tool to reduce phytoplankton abundance (Pires et al. 2005). It is

increasingly clear that for research and management aimed at improving water quality to be

successful, key members of the biological community must be identified and the nature and

mechanisms of their impact on lentic ecosystems must be understood.

Common carp effects on water quality

Numerous studies have documented deleterious effects of common carp on water

quality in aquatic ecosystems, and water quality improvements on rare occasions when carp

control is achieved (Lougheed and Chow-Fraser 2001; Schrage and Downing 2004; Driver et

al. 2005). Carp resuspend sediments while foraging for benthic invertebrate prey, and

resulting turbidity reduces benthic photosynthesis and macrophyte (i.e., vascular plant)

growth (Chumchal et al. 2005). Furthermore, macrophytes are physically uprooted by

foraging carp (Tyron 1954; Crivelli 1983) and due to carp related bioturbation, light

penetration can be significantly reduced, limiting aquatic macrophyte production (Threinen

and Helm 1954). Macrophytes clarify water by reducing wind-induced resuspension of

sediments, by trapping these particles, and by absorbing and sequestering nutrients otherwise

used by phytoplankton (Scheffer 1998; Egertson and Downing 2004). The declines of

aquatic macrophytes due to carp can decrease the resilience of shallow lakes to wind and boat wake induced disturbance of the bethos (Scheffer 1998). By reducing benthic

invertebrate abundance through predation and limiting macrophyte habitat, carp remove

organisms critical in sequestering and mineralizing organic matter and nutrients (Egertson

and Downing 2004). Additionally, carp typically attain high densities and biomass, and 4

excrete large quantities of nutrients (King et al. 1997; Driver et al. 2005). Liberated nutrients can promote toxic phytoplankton blooms (e.g., cyanobacteria) that ultimately can cause fish kills and reduced dissolved oxygen due to microbially-mediated decomposition of phytoplankton cells (Robertson et al. 1997; Parkos et al. 2003; Egertson and Downing 2004;

Ibelings et al. 2005).

Zebra mussel effects on water quality

Zebra mussels are well-known invaders of the North American Great Lakes and major rivers, and are appearing with increased frequency in small inland lakes and reservoirs

(Frischer et al. 2005; Padilla 2005; Butkas and Ostrofsky 2006; Johnson et al. 2006). Like common carp, invading zebra mussels typically experience explosive population growth and function as “ecosystem engineers,” causing dramatic shifts in water quality and other ecosystem attributes (Chase and Bailey 1999; Drake and Bossenbroek 2004). Relative to carp, however, it is difficult to predict whether net zebra mussel impacts on water quality of an invaded lake will be negative or positive. Zebra mussels are effective filter feeders that can reduce turbidity by removing inorganic sediments and phytoplankton and other organic matter from pelagic habitats (MacIsaac et al. 1999; Johannsson et al. 2000; Garton et al.

2005). Much of this material is deposited in benthic habitats in the form of pseudofeces or feces, where it is buried or consumed by other invertebrates and microorganisms (Karatayev et al. 1997; Stewart et al. 1998; Strayer et al. 1999; Haynes et al. 2005). In many shallow areas, increased water clarity following zebra mussel invasions has stimulated increased biomass of benthic algae and macrophytes (Mayer et al. 2002; Garton et al. 2005). Similarly, zebra mussels often generate increased benthic invertebrate and algal abundance and diversity by redirecting nutrients and organic matter from pelagic to benthic habitats, and by 5

increasing habitat in the form of mussel shells (Stewart et al. 1998; Ricciardi 2003; Haynes et

al. 2005). Benthic algae, macrophytes, zebra mussels, and other benthic invertebrates can all

improve water quality by trapping, absorbing, storing, and processing large quantities of

sediments and associated nutrients (Egertson et al. 2004; Baines et al. 2005).

Unfortunately, zebra mussel effects on water quality are not always favorable. These

organisms selectively consume energy- and nutrient-rich diatoms, and excrete large

quantities of phosphorus that return to the water column if not sequestered by benthic

organisms (Vanderploeg et al. 2001; Conroy et al. 2005). By increasing water-column phosphorus concentrations, reducing nitrogen:phosphorus ratios, and reducing planktonic diatom biomass, zebra mussels can promote cyanobacterial blooms (Vanderploeg et al. 2001;

Conroy et al. 2005).

Of particular concern in some areas of the North American Great Lakes are post- zebra mussel invasion increases in abundance of the cyanobacterium Microcystis

(Vanderploeg et al. 2001; Bierman et al. 2005; Conroy et al. 2005; Ouellette et al. 2006;

Rinta-Kanto and Wilhelm 2006). Microcystis is a poor-quality food for aquatic invertebrates,

and can produce a hepatotoxin (microcystin) that poisons aquatic organisms, domestic

animals, and humans if ingested (Carmichael 1996; Vanderploeg et al. 2001; Bierman et al.

2005; Conroy et al. 2005; Ouellette et al. 2006; Rinta-Kanto and Wilhelm 2006).

Additionally, unlike most algae, Microcystis cells are equipped with gas vacuoles that

promote return to the water column after rejection by filter-feeding mussels (Vanderploeg et

al. 2001; Raikow et al. 2004). Cascading effects of increased Microcystis abundance, in

combination with reduced abundance of nutritious phytoplankton, can result in declines in

zooplankton and planktivorous fish abundance, as well as benthic invertebrates dependent on 6

detrital rain (Lozano et al. 2001; Vanderploeg et al. 2001; Dobiesz et al. 2005; Garton et al.

2005). Microcystis blooms might also reduce abundance and production of macrophytes and benthic algae through shading (Pillsbury et al. 2002). Because few zooplankton and benthic invertebrates consume Microcystis and other cyanobacteria, oxygen crashes caused by microbially-mediated decomposition of these algae cells are an added threat to water quality

(Vanderploeg et al. 2001; Raikow et al. 2004).

Water quality in Clear Lake

Clear Lake is a valuable natural resource for Iowa (Downing et al. 2001). Clear Lake is the third largest natural lake in the state, and has been a popular tourist attraction for over a century. Camping, picnicking, boating, fishing, swimming and other recreational activities are popular there, and the annual economic impact on the City of Clear Lake and surrounding area is enormous, estimated to be 43 million dollars (Azevedo et al. 2001; CARD 2008).

Improving and stabilizing the water quality of Clear Lake is a top priority of the Clear Lake community and the Iowa Department of Natural Resources.

Clear Lake is also a highly valuable fishery resource for Iowa, valued at 1-2.5 million dollars annually (Wahl 2001). The lake’s long history as a popular and quality fishery reflects its history as a tourist destination. In recent years, Clear Lake has received attention primarily for its walleye and yellow bass fisheries, although historically it also supported excellent northern pike, largemouth bass, bluegill and crappie fisheries. Improving and diversifying the fishery of Clear Lake are also priorities of the Clear Lake community and the

Iowa Department of Natural Resources. Water quality improvements will be needed for this goal to be achieved. 7

In the last 60 years, Clear Lake water quality has steadily declined (Downing et al.

2001). Suspended sediments and nutrients levels have increased, phytoplankton

concentrations have increased, water clarity has decreased, lake depth has decreased due to

sedimentation, and macrophytes have disappeared from most areas (Niemeier and Hubert

1986; Anthony and Downing 2003; Egertson et al. 2004). Summer phytoplankton

assemblages are now dominated by cyanobacteria, including Microcystis (Iowa Lakes

Information System 2005). Equally distressing was the discovery that groundwater

phosphorus concentration in outwash deposits of the Clear Lake watershed averages 212 µg-

1, much higher than proposed phosphorus standards for surface waters (Burkart et al. 2004).

Accompanying water quality changes are changes in the fish community. Particularly noteworthy is the decline of the sunfish/bass (Centrarchidae) assemblage, which has been replaced by a community of benthic fish dominated by common carp. These fish community changes are strongly associated with decline in water clarity and disappearance of submersed macrophytes, the latter of which are necessary for foraging and reproductive success of many native centrarchid species (Wahl 2001).

Common carp are both symptomatic of decline in overall environmental health of

Clear Lake and a potential roadblock to improvement (Wahl 2001; Schrage and Downing

2004). As discussed above, carp resuspend large amounts of sediment into the water column,

and this combined with physical uprooting of macrophytes and consumption of benthic invertebrates results in reduced water clarity, habitat, and food for other fish species

(Bonneau 1999; Schrage and Downing 2004). The high carp biomass also results in high internal loading of dissolved nutrients through excretion, effectively transferring nutrients from the sediments back into the water column (Lamarra 1975; Chumchal et al. 2005). Carp 8

essentially constitute a positive feedback mechanism, whereby biomass and the deleterious

influence of carp increases as water quality continues to deteriorate. The natural mechanism

of predatory control by piscivorous fish species is also lost as their abundance declines with water quality (Bonneau 1999).

Recently the water quality story of Clear Lake entered a new chapter. In August,

2005, a few adult zebra mussels were found in Clear Lake (J. Wahl, Iowa Department of

Natural Resources, personal communication). By August 2006, small numbers of zebra mussels were found at five of 10 stations surveyed in the lake (K. Bogenschutz, Iowa

Department of Natural Resources, personal communication). Revisits of the same ten sites in

2007, found zebra mussels occupying all ten sites. Over the last several years, the numbers of zebra mussels have drastically increased in abundance and were found on the majority of nearshore substrate including docks and boat lifts (Colvin et al. 2010). Similar to zebra mussel population growth trajectory in other North American lakes, an increase in densities occurred within three years (Mayer et al. 2002; Mills et al. 2003; Nalepa et al. 2003). Even if zebra mussels had not invaded by 2005, models projecting their spread in North America suggested virtual certainty of their arrival in Clear Lake (Drake and Bossenbroek 2004;

Whittier et al. 2007).

How will zebra mussels affect water quality in Clear Lake? We can gain insight from the limited number of studies conducted in other small, shallow, eutrophic North American lakes. Like Clear Lake, benthic habitat in these lakes is characterized by large areas of mud/silt substrate interspersed with sand, gravel and rock. Soon after zebra mussel invasion of Oneida Lake, New York, water clarity, abundance of benthic macroinvertebrates and

macrophytes, and benthic primary production increased, whereas phytoplankton and 9

zooplankton biomass declined (Horgan and Mills 1997; Idrisi et al. 2001; Mayer et al. 2002).

Total primary productivity did not change after zebra mussel invasion of Oneida Lake, as increased benthic primary productivity compensated for reduced pelagic primary productivity (Idrisi et al. 2001). Total phosphorus concentrations in the pelagic zone remained unchanged, although ratio of dissolved phosphorus:total phosphorus increased due to removal of particulate matter by zebra mussels (Idrisi et al. 2001). Similar ecosystem responses were observed after the zebra mussel invasion of Hargus Lake, Ohio (Yu and

Culver 2000). Water clarity increased as phytoplankton biomass and other suspended organic matter concentrations increased (Yu and Culver 2000). Finally, zebra mussels were first discovered in Lake Winnebago, Wisconsin in 1998, and by 2002 reached densities of

20,000 individuals m-2 (J. Kirk, Wisconsin Department of Natural Resources, unpublished data). Quantities of particulate matter in the water column declined significantly following the mussel invasion, as reflected in dramatic declines in phytoplankton biomass (Wisconsin

Department of Natural Resources 2004).

Results from these studies suggest that net zebra mussel effects on water quality in lakes similar to Clear Lake can be neutral or positive. Although water quality in Clear Lake may improve as zebra mussel abundance increases, critical information needed to make an accurate prediction of water quality responses in this and similar ecosystems is lacking.

Effects of invading zebra mussels on water quality in small agriculturally impacted lakes with established Microcystis populations are unstudied. Also unanswered is the question of how zebra mussels affect water quality in hypereutrophic, carp-infested lakes. Will zebra mussel activities (e.g., removal of particulate matter by filter feeding) compensate for or negate adverse carp effects (e.g., sediment resuspension), or do mussels cause even greater 10

water quality problems (e.g., through phosphorus excretion) than already exist in these

ecosystems?

In summary, alternative scenarios exist in regard to overall zebra mussel effects on

water quality in Clear Lake and similar ecosystems vulnerable to mussel invasions. Zebra

mussels might improve water quality by redirecting organic matter and nutrients from the

water column to benthic habitats, stimulating increased production of benthic organisms that

absorb, sequester, and process these and other pollutants. However, zebra mussels might adversely affect water quality by increasing nutrient concentrations and favoring cyanobacterial phytoplankton blooms. By facilitating Microcystis and other cyanobacteria,

zebra mussels may indirectly reduce abundance and production of macrophytes, benthic

algae and invertebrates, fishes, and other critical biological components that maintain high

water quality through trophic interactions and metabolic activities.

Dissertation organization

This dissertation is organized into four chapters that will be submitted to scientific

journals. Chapter 2 extends continuous biomass dynamics to account for pulse commercial

harvest and consequences for parameter estimation on a simulated population of common

carp. Chapter 3 fits an exponential pulse harvest biomass dynamics model to a common carp

population in Clear Lake, Iowa. Chapter 4 uses a food web model (ECOPATH) to evaluate

the impacts of common carp and zebra mussels in the Clear Lake food web. Chapter 5

develops a simulation model (Clear Lake Ecosystem Simulation Model, CLESM) to evaluate

the potential impacts of non-native common carp and zebra mussels on water quality and

recreational fishery yields. Chapter 6 provides a brief closing summary.

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CHAPTER 2. SEMI-DISCRETE BIOMASS DYNAMIC MODELING: AN

IMPROVED APPROACH FOR ASSESSING FISH STOCK RESPONSES TO

PULSED HARVEST EVENTS

A paper submitted to the Canadian Journal of Fisheries and Aquatic Science

Michael E. Colvin, Clay L. Pierce, and Timothy W. Stewart

ABSTRACT

Continuous harvest over an annual period is a common assumption of continuous

biomass dynamics models (CBDMs); however fish are frequently harvested in a pulsed

manner. We developed semi-discrete biomass dynamics models (SDBDMs) that allow

discrete harvest events and evaluated differences between CBDMs and SDBDMs using

an equilibrium yield analysis with varying levels of fishing mortality (F). Equilibrium

fishery yields for CBDMs and SDBDMS were similar at low fishing mortalities and

diverged as F approached and exceeded maximum sustained yield (FMSY). Discrete

harvest resulted in lower equilibrium yields at high levels of F relative to continuous

harvest. The effect of applying harvest continuously when it was in fact discrete was

evaluated by fitting CBDMs and SDBDMs to time series data generated from a

hypothetical fish stock undergoing discrete harvest and evaluating parameter estimates

bias. Violating the assumption of continuous harvest resulted in biased parameter

estimates for CBDM while SDBDM parameter estimates were unbiased. Biased

parameter estimates resulted in biased biological reference points derived from CBDMs.

Semi-discrete BDMs outperformed CBDMs and should be used when harvest is discrete,

when the time and magnitude of harvest are known, and when F is greater than FMSY.

KEYWORDS

biomass dynamics, surplus production, continuous, semi-discrete, biological reference points, common carp Cyprinus carpio, pulse harvest, discrete harvest

INTRODUCTION

Biomass dynamics models (BDMs) are the simplest stock assessment models used to manage fish stocks when stock data are limited to biomass harvested and biomass estimates or indices. Compared with more complex stock assessment models biological realism is simplified with BDMs because population structure data (e.g., age, length) are not considered (NRC 1998). Despite simplified biological realism BDMs are a convenient assessment approach which in some cases, have outperformed more sophisticated age- or stage-structured assessments (Ludwig and Walters 1985; Ludwig and Walters 1989).

Continuous biomass dynamics models (CBDMs) predict biomass at any time and take the form of an ordinary differential equation (ODE): dB/dt = f(B)–C, where dB/dt is the change in biomass (B) over time, f(B) is the biomass production function, and C is the amount of biomass harvested (Polacheck et al. 1993). Biomass production is a function of biomass, representing the net effect of population processes (e.g., somatic growth, recruitment, mortality) on change in biomass over time (Schaefer 1954; Hilborn and

Walters 1992; Prager 1994). The exponential model is the simplest CBDM producing a range of biomass dynamics (i.e., increasing, decreasing, no change) using the production function f(B) = r•B. The exponential model provides a way to represent biomass dynamics in situations where there are insufficient data to resolve more complex models which require additional parameters (e.g., Bmax). However, since the exponential model

does not limit biomass production; unrealistically high biomass predictions can result.

Therefore it provides limited utility and predictive ability for fisheries management.

The flexible trajectory of the exponential model provides a foundation for more

complex BDMs that include density-dependent constraints to limit production. The

Schaefer (Schaefer 1954), Fox (Fox 1970), and Pella-Tomlinson (Pella and Tomlinson

1969) models are common BDMs used to assess fish stocks that include a carrying

capacity parameter, but vary in underlying assumptions of how production is related to

biomass. In addition to continuously operating population processes, a common

assumption of all CBDMs is continuous application of harvest over an annual period,

which has been identified as a shortcoming of CBDMs (NRC 1998).

Accurate assessment of parameters representing stock biomass dynamics is important for managing fisheries. Parameter estimates from CBDMs are used to calculate biological reference points (i.e., MSY, BMSY, FMSY, F0.1) used to manage fish stocks, where maximum sustained yield (MSY) is a function of stock productivity, FMSY is the fishing mortality (F) that maximizes sustained yield, and BMSY is the resulting standing biomass of a stock being harvested at MSY (Cadima 2003). To prevent overfishing, harvesting at MSY has been reduced by finding the fishing mortality that is 10% of the slope of the production curve at the origin (F0.1) and using that value to set harvest limits.

Violating model assumptions can bias parameter estimates, resulting in biased biological reference points which may have consequences for fish stock management. For example,

Polacheck et al. (1993) found that incorrectly assuming a population was in equilibrium prior to fishing resulted in overestimating potential yield and optimum effort. The effect of assuming continuous harvest of fish stocks actually undergoing discrete harvest on

24 parameter estimates and biological reference points is uncertain and worthy of examination.

Inland freshwater commercial fisheries intended to reduce over-abundant

(hereafter referred to as nuisance) fish species represent an extreme discrete application

of harvest within an annual period. Commercial fisheries are commonly used to

minimize the effects of common carp (Cyprinus carpio) on water quality in aquatic

systems (Cahoon 1953; Arlinghaus and Mehner 2003; Chumchal et al. 2005).

Commercial fisheries for common carp are unique because they can occur over a range of

aquatic system areas (100 ha to more than 10,000 ha) with short duration (< 5 d) harvests,

often during spring and fall to minimize thermal-related mortality of sport fish by-catch

(Rose and Moen 1953). Infrequent, but intense harvest events of common carp contrast

sharply with the continuous assumptions of CBDMs. Biomass dynamics characterized

by large instantaneous decreases rather than smooth gradual changes over time are an

ideal situation to evaluate the assumption of continuous harvest (Figure 1).

Discrete fishery harvest is not limited to nuisance fishes. There are several

conditions where fish are harvested over a very short period of time within a year, and

therefore should be treated as a pulsed harvest. Many species are harvested during

migration periods. For example, white sucker Catostomus commersoni commercial

fisheries in Maine typically occur in the spring where traps intercept sexually mature fish

for use as lobster bait (M. Colvin personal observation). Additionally, anadromous fishes

are harvested over a short season (i.e, 1-2 weeks) in coastal river systems when excesses

allow. Flesh and caviar fisheries can occur over a relative short period of time when

mature females are abundant. For example, a paddlefish Polyodon spathula snagging

fisheries on the Missouri River occurs over a 2 week span while fish are sexually mature

and vulnerable to exploitation (Jennings and Zigler 2000; Mestl and Sorensen 2009).

Additionally, some fish stocks can experience a combination of discrete and continuous

fishing mortality. For example, tribal walleye harvest by spearing occurs within a few

weeks in the spring while traditional recreational angling occurs over the annual period in

the ceded territories of Wisconsin (Hansen et al. 2010).

Difference models can be used to represent discrete biomass dynamics

phenomena (Hilborn and Walters 1992), but small time steps (e.g., daily, weekly) are

needed to realistically model infrequent, intense bouts of harvest associated with nuisance

species. Altering model time step to days or weeks results in parameters that may be

difficult to interpret and apply as the majority of inland fisheries statistics are primarily

available as annual values (Allen and Hightower 2010). Even with sufficiently short

discrete time steps the net effects of processes such as predation, growth, recruitment, and

senescence on production may be more realistically modeled continuously rather than

discretely. Neither continuous nor discrete (i.e., difference) BDMs adequately

accommodate discrete harvest in a way that would result in useful biological reference

points. A framework allowing for a combination of continuous and discrete biomass

dynamics would increase biological realism by representing discrete harvest within

continuous population processes governing biomass production.

Semi-discrete models are a hybrid class of models that can be used to simultaneously represent continuous processes and discrete events (Mailleret and Lemesle

2009). However, semi-discrete models have rarely been used in biological modeling or applied management settings because solving these models requires complex algebra and

26 calculus to integrate equations over time and derive analytical solutions, if they exist at all

(Mailleret and Lemesle 2009). Advanced computer-based numerical integration has facilitated continuous ODE solutions to accommodate discrete events (e.g., harvest) and semi-discrete modeling approaches (e.g., R package deSolve Soetaert et al. 2010).

We developed semi-discrete biomass dynamics models (SDBDMs) to evaluate the consequences of assuming continuous harvest when harvest was occurring discretely by fitting CBDMs and SDBDMs to a simulated common carp stock. We simulated fishery yield for 9,999 years for CBDMs and SDBDMs, to compare yield in year 10,000 (i.e., approximate equilibrium, year to year equilibrium) and biological reference points at varying levels of F. Consequences of estimated parameter bias on biological reference points were evaluated by comparing biological reference points calculated from parameter estimates for CBDMs with numerically derived reference points from the equilibrium analysis. Specific objectives of this study were to (i) develop semi-discrete BDMs (i.e., exponential, Schaefer, Fox, Pella-Tomlinson, Schaefer model with an index of biomass and catch per unit effort), (ii) evaluate differences in equilibrium yield and biological reference points between continuous and semi-discrete Schaefer, Fox, and Pella-

Tomlinson BDMs, (iii) evaluate how assuming continuous harvest when it was actually discrete biases parameter estimates of BDMs, and (iv) evaluate consequences of incorrectly assuming continuous harvest on biological reference points (i.e., MSY, FMSY,

BMSY) when harvest is in fact discrete.

METHODS

BDMs

Five CBDMs were extended to accommodate discrete commercial fishery harvest

(Table 1). The simplest was an exponential model which is used in cases of introduced

species or where sufficiently long time series of biomass observations are not available to

fit more complex models. Schaefer, Fox, and Pella-Tomlinson models, used in systems

where a carrying capacity parameter (Bmax) limits production, were also used. A Schaefer model including an additional index of biomass, catch per unit effort (CPUE), was also developed (hereafter SchaeferCPUE). Models are presented as ODEs of the general form

dB(t)/dt = f(B(t))–CC(t) where dB(t)/dt is the change in biomass (kg/ha) over the time

step dt, f(B(t)) is the function relating production to biomass at time t, and CC(t) is the

speed of continuous biomass harvest (kg/ha/year) at time t. Semi-discrete model notation

used in this paper is based on Mailleret and Lemesle (2009) and Zhang et al. (2006).

CBDMs and SDBDMs used for in these analyses are detailed in Table 1 and an

explanation of symbols in Table 2.

Solving BDMs

Numerical integration was required to solve CBDMs and SDBDMs for given

values of rates (r), parameters (Bmax, p), scalars (q) and initial biomass (B0). Among

numerical integrators (e.g., Euler, Runge-Kutta), the Livermore integration routine is the

most accurate (Stevens 2009) and was used to solve BDMs in this study. Numerical

integration was performed using the deSolve package (Soetaert and Herman 2009;

Soetaert et al. 2010) for the R program (R Development Core Team 2010). Values of

initial biomass (B0), rates (r), scalars (q), and parameters (Bmax, p) are required to solve

the ODEs by numerical integration and values used in all subsequent analyses will be

presented in the following sections.

A hypothetical common carp stock

A hypothetical stock of common carp was simulated to evaluate CBDMs and

SDBDMs, based on a real stock of common carp in Clear Lake, Iowa, undergoing discrete commercial harvest (Colvin et al. 2010). Clear Lake has supported a commercial fishery for nuisance common carp since the early 1930s (Bailey and Harrison 1945).

Dates and amounts of commercial fishery harvest have been reported since 1980.

Harvest amounts have varied from 0.1 to 51 kg/ha during spring and 0 to 19 kg/ha during fall events. Identification of common carp aggregation areas in space and time by Penne and Pierce (2008) has increased harvest and reduced the number of within-year harvests over the past four years. Therefore, data from 2007-2010 were used to set up harvest timing and amount of commercial harvest in this simulation study. Annual commercial common carp harvest averaged 35.6 kg/ha (minimum = 8.8 kg/ha, maximum = 58.1 kg/ha), with an average of 86% (minimum = 4%, maximum = 91%) occurring in the spring and 14% (minimum = 8%, maximum = 95%) of harvest occurring in fall. In both seasons harvest occurred over short periods of time (< 5 d). Timing of actual harvest was used to establish temporal harvest structure for simulations and subsequent analyses.

Simulated spring and fall harvest events occurred every 0.2 and 0.8 years respectively. In semi-discrete model notation time of harvest is represented by τk where k indexes when harvest occurred (i.e., τk ∈ {0.2,0.8,1.2,1.8,…,9.2,9.8}). In simulations, 86% of harvest occurred in the spring and 14% during the fall.

Values for r, Bmax, p, and q were selected to generate hypothetical common carp biomass dynamics similar to the real population in Clear Lake. Common carp biomass in

Clear Lake between 1999 and 2010 varied from 124 to 540 kg/ha (Larscheid 2005;

Colvin et al. 2010), therefore a midrange value of 300 kg/ha was used to approximate

average maximum biomass (Bmax). Preliminary estimates of intrinsic growth rate (r) and

catchability (q) of the Clear Lake common carp stock are approximately 0.3 and 0.07 (M.

Colvin unpublished data), respectively, and were used in all models containing

parameters r and q. A value of 1.3 for p in the Pella-Tomlinson model was arbitrarily

selected to cause peak surplus production to occur at a biomass less than half Bmax. A

value of 115 kg/ha was used for B0 in the exponential model and 300 kg/ha was the value of B0 in all other models. The same values for B0, Bmax, r, p, and q were used for subsequent equilibrium yield analysis and to generate biomass dynamics for the hypothetical carp stock undergoing discrete harvest.

Equilibrium yield analysis

An equilibrium analysis was performed to compare the relationship of fishing mortality (F) to equilibrium fishing yield for continuous and semi-discrete Schaefer, Fox, and Pella-Tomlinson models. Analysis was limited to BDMs where a dome-shaped relationship of yield and fishing mortality exists. Equilibrium fishery yields of continuous and semi-discrete Schaefer and Pella-Tomlinson BDMs for F ranging from 0 to 0.30 by 0.01 were calculated by running each scenario until equilibrium was reached.

Since FMSY occurs at the value of r for the Fox model (Cadima 2003), equilibrium yields were evaluated for F ranging from 0 to 1.4. In SDBDMs, F occurred every 0.2 (86% of

F) and 0.8 years (14% of F), simulating previously described seasonal commercial harvests in Clear Lake. The annual fishery yield for year 9,999 was related to annual F to evaluate differences in equilibrium yield between continuous and semi-discrete Shaefer,

Fox and Pella-Tomlinson BDMs. Biological reference points (MSY, BMSY, FMSY, F0.1)

were calculated using equations in Cadima (2003) for CBDMs. A grid search was used

to find MSY, BMSY, and FMSY for SDBDMs Analytical solutions for F0.1 are unavailable

and therefore not calculated.

Parameter bias

Generating known biomass dynamics.—The effect of assuming continuous

harvest when it is actually discrete was assessed by fitting each CBDM and SDBDM

(Table 1) to a simulated common carp stock experiencing discrete harvest events.

Underlying (true) biomass time series were generated from each SDBDM using

previously described parameter values and four values of F to calculate amount of harvest

(DCspring=0.86•F•B, DCfall=0.14•F•B). The exponential model used F values equal to

0.5•r, r, 1.5•r, and 1.75•r. Values of F for remaining BDMs were calculated as 0.5•FMSY,

FMSY, 1.5•FMSY, and 1.75•FMSY, where FMSY is semi-discrete FMSY. Harvest amounts

were calculated as DC(τk) = (γseason•F) •B(τk), where DC(τk) is the harvested biomass in

kg/ha at event τk, γseason is the fraction of annual fishing mortality in a season, F is the

annual fishing mortality, and B(τk) is the biomass at the time of harvest event. Harvested

biomass was summed within year for CBDM inputs.

Simulating observations of biomass and CPUE.—A Monte Carlo simulation was

used to simulate time series of biomass and CPUE observations that were fit to BDMs.

Biomass sampling occurred every 0.3 year (t= 0.3, 1.3, …, 9.3), mimicking the common practice of batch marking common carp captured during spring commercial fishing in

Clear Lake and returning those fish to the lake for mark-recapture population estimates

(Colvin et al. 2010). Observed catch per unit effort (CPUE) occurred every 0.7 year (t=

0.7, 1.7, …, 9.7) simulating fall indexing of biomass by CPUE. For each generated

biomass time series (20 combinations in total, five models, four values of F), 50

replicated time series of biomass observations (10 observations per time series) were

ε simulated using the equation y(t)i = B(t)e , where yi is observed biomass at time t, B(t) is

biomass at sampling time t, and ε is a random, multiplicative, log-normally distributed

observation-only error with loge(mean) = 0 and constant coefficient of variation (CV)

equal to 0.2.

Biomass dynamics models are frequently fit simultaneously to both biomass and

catch per unit effort (CPUE) data. To investigate how assuming continuous fishing

mortality effects parameter estimates of these types of models, an additional 50 time

ε series of CPUE (n=10) were generated for the SchaeferCPUE BDM by I(t)j = q•B(t)•e , where I(t)j is the observed CPUE at time t, q is catchability, B(t) is biomass at sampling

time t, and ε is a random, multiplicative, log-normally distributed observation-only error

with loge(mean) = 0 and CV equal to 20%. Only the SchaeferCPUE model was evaluated

due to the common use of the model and to simplify the analysis. The level of CV was

selected to represent the level of certainty recommended for management (Van Den

Avyle and Hayward 1999).

Parameter estimation.—CBDMs and SDBDMs were fit to each time series of

biomass estimates and CPUE to compare bias in parameter estimates for increasing

harvest. Harvest amount was instantaneously removed in SDBDMs during model fitting.

Total annual harvest was continuously removed over the annual period during model

fitting for CBDMs. Model parameters (e.g., r, Bmax, p, q) were estimated by maximum

likelihood assuming a multiplicative log-normal observation-error structure (Polacheck et

al. 1993; Hilborn and Mangel 1997; Walters and Martell 2004). Maximum likelihood

estimates of model parameters were found using the optim function in R to maximize the

following log likelihood:

2 ⎛ /)(log( ˆ tyty ))( ⎞ ⎛ − ⎜ ii ⎟ ⎞ i ⎜ 1 ⎜ •2 σ 2 ⎟ ⎟ θ ty = log))(( • e ⎝ y ⎠ , (1) l il ∑1 e ⎜ ⎟ ⎜ ty ••2•)( σπ 2 ⎟ ⎝ i y ⎠

where θl is the vector of model parameters (i.e., r, Bmax, p, σy), y(t)i is observed biomass at

time t, ˆ ty )( i is model-estimated biomass at time t, and σy is the standard deviation of the

residuals. The previous log likelihood was used to find values of model parameters that

maximize the log likelihood for the exponential, Schaefer, Fox, and Pella-Tomlinson

models. To include CPUE (I), the log likelihood was modified to:

2 ⎛ /)(log( ˆ tyty ))( ⎞ ⎛ − ⎜ ii ⎟ ⎞ i ⎜ 1 ⎜ •2 σ 2 ⎟ ⎟ θ tIty = log))(,)(( e ⎝ y ⎠ l jil ∑1 e ⎜ ⎟ ⎜ ty ••2•)( σπ 2 ⎟ ⎝ i y ⎠ , (2) 2 ⎛ /)(log( ˆ tItI ))( ⎞ ⎛ −⎜ jj ⎟ ⎞ i ⎜ 1 ⎜ •2 σ 2 ⎟ ⎟ + log e ⎝ I ⎠ ∑1 e ⎜ ⎟ ⎜ tI ••2•)( σπ 2 ⎟ ⎝ j I ⎠

where θl is the vector of model parameters (i.e., r, Bmax, q, σy, σI), y(t)i is observed biomass

ˆ at time t, ˆ ty )( i is model-estimated biomass at time t, I(t)j is observed cpue at time t, tI )( is model-estimated CPUE at time t, and σy and σI are standard deviations of biomass and

CPUE residuals. The initial biomass (B0) was constrained to be equal to Bmax in models

containing Bmax for estimation purposes. This constraint was used since it is possible for

B0 to exceed Bmax in numerical optimization (Prager 1994) and this constraint performed

well for estimating biological reference points even when discrepancies in B0 and Bmax exist (Punt 1990). Quasi-Newton (“BFGS”) non-linear search algorithm was used for all maximizations.

Parameter estimate bias was used to evaluate consequences of applying discrete harvest continuously. Proportional parameter bias was calculated by subtracting the estimated parameter by the true parameter and dividing by the true parameter used to generate the underlying true biomass dynamics for each BDM. A parameter was considered unbiased if replicates were centered on 0. Parameter bias was graphically assessed.

Biological reference points.—Consequences of assuming continuous fishing mortality on biological reference points were evaluated by comparing reference points derived from CBDMs to true values used to generate hypothetical common carp stock biomass dynamics. Biological reference points were calculated from maximum likelihood estimates for parameters of continuous Schaefer, Fox, Pella-Tomlinson, and

SchaeferCPUE BDMs using equations in Cadima (2003). Proportional bias of biological

reference points were calculated as ( ˆ − ) θθθ where θ is the true biological reference

point from the equilibrium analysis and θˆ is the biological reference point calculated

from estimated parameters for CBDMs. A parameter is unbiased if it is centered on 0.

Bias of biological reference points was graphically assessed.

RESULTS

Equilibrium yield.—Equilibrium yield results of continuous and semi-discrete

Schaefer, Fox, and Pella-Tomlinson BDMs provided different biological reference points

despite sharing the same annual fishing mortality and parameter values (Figure 2; Table

3). Equilibrium yields for CBDMs and SDBDMs were similar at low levels of F and

became increasingly divergent as F approached and exceeded FMSY (Figure 2).

Equilibrium yield at high levels of F (F>>FMSY) was reduced for SDBDMs relative to

CBDMs (Figure 2). FMSY was slightly reduced when F occurred discretely compared to

continuous application of F for all three BDMs (Table 3). MSY and Bmsy were slightly

higher when F was applied discretely relative to continuous application.

Parameter bias.—Proportional bias varied with harvest amount and type of BDM.

Median absolute parameter bias was within ±0.1 for the majority of parameters estimated

by semi-discrete exponential, Schaefer, Fox, and SchaeferCPUE BDMs (Figure 3 and 4).

Exceptions were negative biases in estimates of r and Bmax in semi-discrete Schaefer

model and SchaeferCPUE. Negative bias increased for r and B0 with increasing harvest for

the continuous exponential model (Figure 3). Median parameter bias of CBDMs was minimized at low values of F (Figure 3 and 4). The magnitude of median parameter bias

(negative or positive) increased with F for continuous exponential, Schaefer, Fox and

SchaeferCPUE. Median bias of q was less than 0.1 but increased systematically with F.

Median bias of r did not exhibit systematic pattern for either continuous or semi-discrete

Pella-Tomlinson models. Bmax was unbiased (|median bias|<0.1) at low levels of F for

continuous and all levels of F for semi-discrete Pella-Tomlinson BDM. Both continuous

and semi-discrete Pella-Tomlinson BDMs overestimated p, but this bias was minimized

at low F levels.

Biological reference points.—Proportional bias of biological reference points

varied with F and among CBDMs (Figure 5). Negative median proportional bias of BMSY and MSY increased with increasing F for all CBDMs. Bias of FMSY did not vary systematically with F, but the range of proportional bias declined with increasing F.

DISCUSSION

Results of our analyses demonstrate that assuming continuous fishing mortality

when it is occurring discretely has potential management consequences. The SDBDMs

we evaluated reduced parameter estimate bias at high values of F relative to CBDMs for

stocks experiencing discrete harvest. Additional biological realism provided by

SDBDMs did not come at the expense of greater computer time required to fit models.

Maximum likelihood solutions were achieved for both continuous and semi-discrete

BDMs in less than 2-3 minutes for even the most complex models. Explicitly accounting

for discrete harvest structure (date and amount) using SDBDMs outperformed CBDMs

when F approaches and exceeds FMSY. It should be noted that SDBDMs are not more complex in terms of number of parameters, but add biological realism by explicitly accounting for amount and timing of biomass harvest. Knowing when and how much biomass is removed imposes an informative physical constraint when fitting models to time series, since a stock must have sufficient biomass and production to support discrete harvest events (i.e., discrete harvest must be less than standing biomass).

Equilibrium analysis of the Schaefer, Fox, and Pella-Tomlinson BDMs provided insight into differences in effect of fishing mortality on equilibrium yield in continuous and discrete fishing systems. Under low values of F the differences between CBDMs and

SDBDMs were negligible indicating that use of either model type would yield similar

management results. Equilibrium yield at F approaching or greater than FMSY were increasingly different between CBDMs and SDBDMs, indicating that applying harvest continuously when it is actually discrete can influence management recommendations.

Equilibrium yield predicted for high levels of F (i.e., greater than FMSY) was lower for

SDBDMs, which could have management consequences when a fish stock is

experiencing heavy discrete harvest but harvest is modeled continuously in BDMs.

Production over an annual period is less when discrete harvest reduces biomass levels

over a short period, since production is dependent on biomass. In other words, biomass is

reduced by a discrete harvest and the population cannot “catch up” and equal production

of a population where the same amount of biomass was continuously harvested.

Applying discrete F continuously over-estimates equilibrium yield at high values of F

based on our equilibrium analyses. Over-estimating equilibrium yield is obviously

problematic for managing sustainable fisheries and potentially part of the reason for the

long standing discontent with MSY as a biological reference point for managing fish

stocks (Larkin 1977; Punt and Smith 2001). Harvest objectives identified using

equilibrium approximations of continuous biomass dynamics models should result in

over-harvest if harvests are satisfied using discrete commercial fishing efforts.

Overestimating sustained yield is not necessarily a problem with nuisance commercial

fisheries where the objective is to overfish a stock to reduce biomass-dependent impacts.

FMSY was slightly lower when fishing effort was modeled as discrete events rather

than on a continuous basis. This result was observed for all BDMs exhibiting a dome

shaped relationship of equilibrium yield and F used in this study. These discrepancies in

FMSY between CBDMs and SDBDMs are notable in providing a potential basis for use of

F0.1 as a biological reference point. The use of F0.1 has emerged as a useful “rule of

thumb” for managing fisheries, but according to Hilborn and Walters (1992) this is an

arbitrary, ad hoc strategy with no theoretical basis. It is unlikely that fishery harvest is

truly continuous over an annual period, so reducing FMSY calculated from CDBDM

parameter estimates compensates for violating the assumption of continuous fishing

mortality.

Time series are recommended methods for fitting BDMs (Hilborn and Walters

1992). Accurate estimates of parameters such as r and Bmax based on time series are

critical for establishing biological reference points used to manage fish stocks using

output controls (e.g., total allowable catch). Bias in parameter estimates can result in

biased estimates of MSY and FMSY, potentially leading to stock mismanagement. On

average, parameter bias was negative for most biological reference points, which

indicates that reference points may be conservative when fishing harvest is applied discretely but modeled continuously due to underestimating Bmax. Parameter bias in

CBDMs was dependent on how much biomass was harvested. In particular, bias in Bmax

was consistently more severe at higher harvest levels among all CBDMs. In hypothetical

biomass dynamics of SDBDMs (Figure 1), biomass is instantaneously reduced then

increases thereafter. Failure to incorporate information on magnitude or timing of large

biomass harvests in relation to biomass observation results in negative bias in Bmax in

CBDMs. It should be noted that neither CBDMs nor SDBDMs did a good job of estimating model parameters for the Pella-Tomlinson model, where fitting an additional parameter (four parameters in total) relative to the other models resulted in poor fits and the possibility that additional data or reparameterizing may be required.

Fishery harvest dynamics likely exhibit a combination of discrete and continuous fishing mortality. For example, an “opening day” phenomenon can occur when a recreational or commercial fishery opens resulting in eager fishers and greater fishing mortality during the opening day compared to the remaining season. Greater fishing mortality likely occurs on opening day since the amount of fishing effort is high due to the number of casual (i.e., only fish opening day) fishers exploiting vulnerable fish over a short period of time. It is likely that after opening day, fishing effort is dominated by serious fishers. For example, commercial harvest of spiny rock lobster fisheries of

Bahia-de-la-Ascension, Mexico, was found to be greatest on opening day and subsequently decreased over time (Lozanoalvarez et al. 1991). In an inland recreational fishery the majority of fish were harvested during the first two days of opening on a previously unfished lake in Wisconsin (Goedde and Coble 1981). Events such as free fishing days and tournaments could be viewed as discrete harvest events due to angler behavior that occurs in addition to ongoing licensed recreational fishing (i.e., continuous harvest). Overfishing may result if discrete fishing mortality events are not accounted for in assessments of recreational fisheries, due to overestimation of harvest when F exceeds

FMSY. SDBDMs accommodate the potential for biomass dynamics to be modeled as a mixture of discrete and continuous harvest and therefore may provide a more realistic and accurate stock assessment.

Discrete events influencing aquatic animal biomass frequently occur in aquatic systems. One example is the stocking of fish into aquatic systems to serve as biological controls (Lathrop et al. 2002), fishing opportunities (Pine et al. 2007), or promote species conservation (Oosterhout et al. 2005). This discrete input of fish biomass can have

significant food web impacts through an instantaneous increase in the consumption

required to support these newly stocked fish. For example, Pope et al. (2009) found that

trout stocked in alpine lakes altered the alpine lake ecosystem by preying on emerging

insects that would otherwise be a terrestrial subsidy. SDBDMs could be used to more

realistically explore the consequences of stocking timing and amount on prey dynamics

in food web and ecosystem models.

My analysis shows that the use of semi-discrete models to represent discrete

phenomena such as pulsed harvest within the context of continuous mortality and other

population processes can have significant management implications. Discrete

phenomena can result in complex population dynamics that continuous models may not

adequately represent. Periodic (i.e., seasonal, disturbance) mortality events can have a

significant influence on population dynamics. For example, large snow events can cause

mortality in ring-necked pheasant hens Phasianus colchicus (Perkins et al. 1997) or high

flow events can cause mortality in juvenile coho salmon Oncorhynchus kisutch (Ebersole

et al. 2006). Winter- and summer-fish kills where large mortality occurs over a short

period of time due to decreased dissolved oxygen levels (Hurst 2007) could be

accommodated in BDMs as discrete events. Biomanipulation is a common restoration

technique to restore water quality (e.g., Schrage and Downing 2004) and removal of

common carp by rotenone would be more realistically modeled using SDBDMs than

CBDMs. Other disturbances such as reproductive failure within a year (e.g., Carlander

1958) or disease epidemics represent discrete events that could have large influences on biomass dynamics over short periods of time. Additionally, conservation and supplementation stocking can be discrete efforts occurring for a number of aquatic and

terrestrial species. The manuscript focuses on a pest population of common carp as an

example, however this approach could also be applied to pest suppression occurring in

agroecoystems (i.e., predator releases, pesticides) (Lu et al. 2003; Nundloll et al. 2010).

Accounting for these events occurring over a short period of time relative to the annual

period using semi-discrete models can potentially improve understanding and

management of population dynamics in a variety of systems and circumstances.

ACKNOWLEDGMENTS

We thank Scott Grummer (IADNR) for providing common carp harvest data.

This manuscript was improved by critical comments from Christopher Chizinski,

William French, and two anonymous reviewers. This project was supported in part by

the Department of Natural Resource Ecology and Management at Iowa State University,

Iowa Cooperative Fish and Wildlife Research Unit, and the Iowa Department of Natural

Resources. The use of trade names or products does not constitute endorsement by the

U.S. Government.

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300 Pulsed Schaefer Schaefer

250 ) 1 −

200 Biomass ha (kg

150

100

246810

Year

Figure 1. Hypothetical fish stock biomass dynamics undergoing continuous (dotted line) and discrete (solid line) commercial harvest. Model parameters and annual harvest are the same for both models but harvested biomass is removed continuously in the continuous model and discretely in the semi-discrete model.

25 A: Schaefer Continous Semi-discrete 20

Semi-discrete FMSY Continuous FMSY 0

0.00 0.05 0.10 0.15 0.20 0.25 0.30 B: Fox )

1 30 − 25

5 Semi-discrete F Continuous F 0 MSY MSY Equilbrium yieldha (kg 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 C: Pella-Tomlinson 20

Semi-discrete FMSY Continuous FMSY 0

0.00 0.05 0.10 0.15 0.20 0.25 0.30

−1 −1 Fishing mortality (kg ha yr ) Figure 2. Equilibrium fishing yield over a range of fishing mortalities for continuous (dotted line) and semi-discrete (solid line) Schaefer (top panel), Fox (middle panel), and Pella-Tomlinson biomass dynamics models. Vertical lines denote location of FMSY for CBDMs (dotted line) and SDBDMs (solid line).

B: Exponential (B ) A: Exponential (r) Continuous 0 0.4 Semi-discrete 0.4

0.2 0.2

0.0 0.0

-0.2 -0.2

-0.4 -0.4

-0.6 -0.6

2.5 C: Schaefer (r) D: Schaefer (Bmax) 0.2 2.0 0.1 1.5

1.0 0.0

0.5 -0.1

0.0 -0.2

Proportional biasProportional -0.5 -0.3 -1.0

2.5 E: Fox ( r ) F: Fox (Bmax)

2.0 0.2 1.5

1.0 0.0

0.5 -0.2 0.0

-0.5 -0.4 -1.0

Low Medium High Very high Low Medium High Very high Fishing mortality Figure 3. Boxplots of proportional bias for parameters estimated for exponential (r; panel A, B0 panel B), Schaefer (B0 panel C, and Bmax panel D), and Fox (B0 panel E, and Bmax panel F) CBDMs (no shade) and SDBDMs (shaded). The solid horizontal line in the boxes represents the medians. Boxes represent the bounds of the 25th and 75th quartiles of the data. Whiskers represent the lower and upper bounds of the data. The horizontal dotted line denotes a proportional bias value of zero.

B: Pella-Tomlinson (B ) 2.5 A: Pella-Tomlinson (r) Continuous max Semi-discrete 2.0 0.4

1.5 0.2 1.0

0.5 0.0

0.0 -0.2 -0.5

-1.0

1.5 C: Pella-Tomlinson (p) E: SchaeferCPUE (r) 2.5

2.0 1.0 1.5

1.0 0.5 0.5

0.0

Proportional biasProportional 0.0 -0.5

E: SchaeferCPUE (Bmax) E: SchaeferCPUE (q) 0.4 0.4

0.2 0.2

0.0 0.0

-0.2 -0.2

-0.4 -0.4

Low Medium High Very high Low Medium High Very high Fishing mortality Figure 4. Boxplots of proportional bias for parameters estimated for Pella-Tomlinson (r panel A, Bmax panel B, and p panel C) and SchaeferCPUE (B0 panel D, Bmax panel E, and q panel F) CBDMs (no shade) and SDBDMs (shaded). The solid horizontal line in the boxes represents the medians. Boxes represent the bounds of the 25th and 75th quartiles of the data. Whiskers represent the lower and upper bounds of the data. The horizontal dotted line denotes a proportional bias value of zero.

A: FMSY 3 Schaefer Fox Pella-Tomlinson SchaeferCPUE

-1

B: B 0.6 MSY Schaefer Fox Pella-Tomlinson SchaeferCPUE 0.4

0.2

0.0

-0.2

Proportional biasProportional -0.4

2.5 C: MSY Schaefer Fox Pella-Tomlinson SchaeferCPUE 2.0 1.5

1.0

0.5 0.0 -0.5

-1.0

y y /2 sy /2 s /2 s /2 sy Fms y m ms y Fm ms y ms y ms y m ms y ms y Fm ms y = = =F 5F ms y F .5F ms y F 5F ms y 5F F ms y F .5 : 1 .7 7 1 7 m m: 1 : F=F u : F=F u : F=F um: F=F um: F= i F= i F=1. w: F= i : F=1. : F=1.75F di ow o Low igh: F= h Low h: L h: L igh h: Med H Me High: F=1.5Fig Med High: F=1.5F Med H Hig H Hig Hig ry ry ry ry e e Ve V V Ve

Figure 5. Boxplots of proportional bias for biological reference points FMSY (panel A), BMSY (panel B), and MSY (panel C) calculated for CBDMs. The solid horizontal line in the boxes represents the medians. Boxes represent the bounds of the 25th and 75th quartiles of the data. Whiskers represent the lower and upper bounds of the data. The horizontal dotted line denotes a proportional bias value of zero.

Table 1. Continuous and semi‐discrete biomass dynamics models used in this paper. Model Continuous Semi‐discrete

Exponential tdB )( tdB )( ⎫ −= tCCtBr )()(• = ),(• ttBr ≠ τ ⎪ dt dt k ⎬ + ⎪ k k −= k ),()()( tDCBB =ττττk ⎭

Schaefer tdB )( ⎛ − tBB )( ⎞ tdB )( ⎛ − tBB )( ⎞ ⎫ = tBr •)(• ⎜ max ⎟ − tCC )( = tBr •)(• ⎜ max ⎟, t ≠ τ ⎪ ⎜ ⎟ ⎜ ⎟ k dt ⎝ Bmax ⎠ dt ⎝ Bmax ⎠ ⎬ + ⎪ k k −= DCBB τττk ),()()( t =τ k ⎭ Fox tdB )( ⎛ tB )( ⎞ tdB )( ⎛ tB )( ⎞ ⎫ = tBr ⎜ − log1•)(• ⎟ − tCC )( = tBr ⎜ − log1•)(• ⎟, t ≠ τ ⎪ ⎜ e ⎟ ⎜ e ⎟ k dt ⎝ Bmax ⎠ dt ⎝ Bmax ⎠ ⎬ + ⎪ k k −= DCBB τττk ),()()( t = τ k ⎭ 52 Pella‐Tomlinsona p−1 p−1 tdB )( r ⎛ tB )( ⎞ tdB )( r ⎛ tB )( ⎞ ⎫ = tB ⎜1•)(• − ⎟ − tCC )( = tB ⎜1•)(• − ⎟ , t ≠ τ ⎪ ⎜ ⎟ ⎜ ⎟ k dt p ⎝ Bmax ⎠ dt p ⎝ Bmax ⎠ ⎬ + ⎪ k k −= DCBB τττk ),()()( t =τ k ⎭ SchaeferCPUE tdB )( ⎛ − tBB )( ⎞ tdB )( ⎛ − tBB )( ⎞ ⎫ = tBr •)(• ⎜ max ⎟ − tCC )( ⎜ max ⎟ ⎜ ⎟ = tBr •)(• ⎜ ⎟, t ≠ τ k ⎪ dt Bmax dt B ⎬ ⎝ ⎠ ⎝ max ⎠ + ⎪ = tBqtI )(•)( k k −= DCBB τττk ),()()( t =τ k ⎭ = tBqtI )(•)( a The term r/p is eliminated when p =0

1 Table 2. List of symbols, descriptions, and units. Symbol Description + τ k Time when instantaneous harvest occurs (year) t Continuous time (year)

τ k Discrete time (year) i Index of biomass observations j Index of CPUE observations k Index of events l Index of model parameters tB )( Biomass at time t (kg•ha-1) tI )( Observed catch per unit effort at time t (kg•effort-1) -1 ˆ tI )( Predicted catch per unit effort at time (kg•effort ) ty )( Observed biomass at time t (kg•ha-1) ˆ ty )( Predicted biomass at time t (kg•ha-1) tCC )( Speed of continuous catch at time t (kg•ha-1•year-1) -1 DC τ k )( Discrete catch at harvest event τ k (kg•ha ) r Intrinsic growth rate (kg•ha-1• year -1) -1 Bmax Maximum biomass (kg•ha ) q Catchability (kg•ha-1•effort-1) p Asymmetry parameter (dimensionless) F Fishing mortality (year-1)

Table 3. Biological reference points for continuous and semi-discrete biomass dynamics models. Model Model type MSY Bmsy Fmsy F0.1 Schaefer Continuous 22.50 150.0 0.150 0.136 Semi-discrete 22.42 151.3 0.142 Fox Continuous 33.11 110.5 0.300 0.236 Semi-discrete 32.88 112.8 0.267 Pella-Tomlinson Continuous 20.62 158.1 0.130 0.120 Semi-discrete 20.56 159.2 0.124

CHAPTER 3. STRATEGIES TO CONTROL A COMMON CARP (CYPRINUS

CARPIO) POPULATION BY PULSED COMMERCIAL HARVEST

A paper submitted to the North American Journal of Fisheries Management

Michael E. Colvin, Clay L. Pierce, Timothy W. Stewart, and Scott E. Grummer

ABSTRACT

Commercial fisheries are commonly used to manage nuisance fishes in freshwater

systems, but often unsuccessfully. We evaluated strategies for successfully controlling a

nuisance common carp (Cyprinus carpio) population by pulsed commercial harvest using a

combination of field sampling, population estimation and CPUE indexing, and simulation

using an exponential semi-discrete biomass dynamics model (SDBDM). The range of annual

fishing mortalities resulting in successful control were narrow (F=0.244-0.265). Common

carp biomass dynamics were sensitive to unintentional under-harvest due to high rates of

surplus production and a biomass doubling time of 2.7 years. Simulations indicated that

biomanipulation never achieved successful control unless supplemental fishing mortality was

imposed. Harvesting a majority of annual production was required to achieve successful

control, as indicated by the ecotrophic coefficient (EC). Readily available biomass data and

tools such as SDBDMs and ECs can be used in an adaptive management framework to

successfully control common carp and other nuisance fishes by pulsed commercial fishing.

KEYWORDS

biomass dynamics, semi-discrete, discrete harvest, pulsed harvest, common carp

Cyprinus carpio, pest species, nuisance species, commercial fisheries simulation modeling

INTRODUCTION

Commercial fisheries are commonly used to manage over-abundant (hereafter

referred to as nuisance) fishes in freshwater systems. However, these populations are rarely harvested at a rate sufficient for their control (Wydoski and Wiley 1999). Limited success of commercial fisheries to control nuisance populations can be attributed to insufficient harvest which in turn is a result of low landing price or lack of market. Insufficient commercial harvest can also result from lack of information on nuisance fish population dynamics, which is necessary to develop harvest targets that lead to population control. Formal stock assessments for nuisance fishes are scarce in freshwater systems, likely due to their lack of significant economic value. We argue that in the future, assessing nuisance fish dynamics will be critically important for successful nuisance fish control programs that use commercial fisheries.

In North American lentic ecosystems common carp (Cyprinus carpio) can dominate the fish community (Cahn 1929) and adversely affect water quality and biotic assemblages

(Lougheed et al. 1998; Scheffer 1998; Chumchal et al. 2005). Common carp quickly attain large body size in North America (Carlander 1969; Crivelli 1983), and can dominate the fish assemblage biomass of aquatic systems (Cahn 1929). Severity of common carp impacts on aquatic systems are primarily functions of population biomass. Common carp biomass has been positively associated with chlorophyll a, turbidity, and total phosphorus which are symptoms of degraded water quality in aquatic systems (Chumchal et al. 2005). Significant reduction of common carp biomass can quickly shift (< 1 year) a shallow aquatic system from a degraded turbid-water state to a clear-water state with abundant macrophytes, reduced phytoplankton biomass, and increased large-bodied zooplankton (e.g., Daphnia) abundance

(Schrage and Downing 2004). Common carp biomass reductions benefit aquatic vegetation

(Threinen and Helm 1954; Tyron 1954; Bajer et al. 2009), game fish populations (Rose and

Moen 1953; Jackson et al. 2010), water clarity (Schrage and Downing 2004), waterfowl production (Cahoon 1953; Bajer et al. 2009), and local economics (Cahoon 1953). Limiting common carp to a biomass ≤ 100 kg/ha has been identified as a potential management target

that minimizes environmental degradation (Mehner et al. 2004a; Bajer et al. 2009).

Common carp biomass can be reduced using chemical, biological, and physical

removal methods. Common carp removals using chemical piscicides (e.g., rotenone) began

in the 1940s (O'Donnell 1943; Weier and Starr 1950) and remain in use today (e.g., Schrage

and Downing 2004). However, piscicides can be prohibitively expensive and cause non-

target mortalities. Non-target mortality associated with piscicides can require reassembly of

native fish assemblages post treatment, otherwise common carp and other undesirable fishes

will continue to dominate the system (Shapiro and Wright 2007). In systems where non-

target impacts are unlikely or negligible, chemical control methods can result in significant

short-term water quality improvements (Schrage and Downing 2004). However, large-scale

common carp biomass manipulations (hereafter referred to as biomanipulation) rarely result

in long-term (> 8–10 years) water quality improvements without reductions in external

nutrient loadings (Hansson et al. 1998; Beklioglu 2003; Kasprzak et al. 2003; Sondergaard et

al. 2007) and suppression of zooplanktivorous fishes (Sondergaard et al. 2007).

Contemporary biomanipulation strategies incorporate both a top-down approach

manipulating piscivore biomass (Kitchell 1992), and bottom-up approaches where nutrient

recycling is limited by reducing bentivorous fish biomass (Schrage and Downing 2004).

Biological methods such as stocking piscivores to act as biological controls of pest species or to cause a trophic cascade have been used with varying success to improve water quality. Northern pike (Esox lucius) have been used to control nuisance fishes, however their success as biological control agents depends on inclusion of nuisance fishes in their diet

(Paukert et al. 2003; Ward et al. 2008), timing of stocking events, biomass, and body size

(Skov and Nilsson 2007). By controlling nuisance fish populations, piscivores can induce a

trophic cascade that leads to improved water quality, recovery of sport fish populations, and increased biological diversity (Carpenter et al. 1985; Kitchell 1992; Lathrop et al. 2002).

Unfortunately such biological control of common carp biomass by predation has not been

successful without prior biomanipulation (Perrow et al. 1995; Mehner et al. 2004b).

Physical removal of common carp by commercial fisheries can reduce their impacts, but harvest amounts may not be sufficient for population control and elimination of their negative effects. Commercial fisheries can cause non-target impacts (e.g., by-catch); however by-catch can be minimized by fishing gear size and identifying periods and locations of common carp aggregations (e.g., Diggle et al. 2004; Penne and Pierce 2008;

Bajer et al. 2011). Insufficient common carp harvest is most likely due to low landing price

(~ 0.20 USD/kg), resulting in commercial fisherman harvesting just enough biomass to satisfy a limited market (Wydoski and Wiley 1999; Hansen et al. 2010). Harvest subsidies can replace market incentives to increase commercial harvest. Despite newer technologies that may become available in the future (e.g., daughterless carp; Brown and Walker 2004), commercial harvest to control common carp is likely to continue.

Another problem with control of nuisance fish by commercial harvest is related to the uncertainty in the amount of harvest needed for effective control. Biomass harvest targets

that reduce common carp biomass over the long term, yet identifying these targets has not

been attempted in a formal way (e.g., stock assessment). Biomass dynamics models (BDMs)

are simpler than size- or age-structured models, only requiring estimates of biomass (e.g.,

estimates, indices) and harvest amounts (Hilborn and Walters 1992). Harvest targets that reduce biomass to desired levels can be identified using BDMs if sufficient time series data exist. Simulations using a fitted BDM can then be used to evaluate potential management strategies and identify harvest targets that result in successful control. It should be noted that harvest targets of nuisance fishes are different than harvest quotas for commercially important fishes. Commercial fishing stops once a harvest quota is reached, but for nuisance species a harvest target needs to be equaled or exceeded for effective population control.

Alternative management strategies featuring different harvest targets and schedules to control common carp biomass can be evaluated by simulation modeling. Because they simplify the real world to a model, simulations provide harvest targets that do not necessarily account for real world limitations. Practical management can potentially be limited due to, among other things, uncontrollable factors (e.g., weather, personnel limitations) and unintentional under-harvest (i.e., missing the harvest target). Therefore, identifying strategies that are robust to uncertainties inherent in real world management is necessary for successful population management and setting reasonable harvest targets. Success of different scenarios can be evaluated by comparison with an objective, such as maintaining a low standing biomass (e.g., ≤ 100 kg/ha; Bajer et al. 2009). Potential strategies include: 1) doing nothing (i.e., no commercial fishing), 2) market driven commercial harvest (i.e., the status quo), and 3) periodic large-scale removals when a high biomass level (hereafter

nuisance biomass level) is reached (i.e., biomanipulation). Additional simulation analysis

can be used to evaluate the effect of unintentional under-harvest on biomass dynamics.

Time series data required to fit BDMs can be sparse or nonexistent for nuisance

fishes, precluding their use in identification of harvest targets. Ricker (1946) proposed the

ecotrophic coefficient (EC) as a simple biomass-based metric that may be useful for setting

harvest targets based on a single year of data. EC is calculated as the ratio of biomass

harvested and produced over an annual period, varying from 0 in the case of no harvest to

greater than 1 under very heavy harvest. EC has been used to guide development of harvest

quotas for salmonid populations in Minnesota (Waters 1992) and North Carolina (Wallace

2010), but to our knowledge has never been used to develop harvest targets for nuisance

fishes. An EC greater than 0.5 is an unsustainable harvest for salmonid populations (Waters

1992). However, the EC threshold for unsustainable harvest will depend on the fraction of

annual production allocated to surplus production, and thus will vary among species.

In this paper we estimated and indexed (i.e., catch per unit effort; CPUE) common

carp biomass in a shallow eutrophic lake undergoing pulsed commercial harvest. Biomass

estimates and CPUE were then used to fit a semi-discrete biomass dynamics model

(SDBDM) which accommodates pulsed harvest. The fitted SDBDM was then used to

identify harvest targets required to achieve a biomass threshold (i.e., ≤ 100 kg/ha) using

pulsed commercial fishing. Specifically, we: i) estimated biomass using mark-recapture, ii)

indexed common carp biomass by trawling offshore lake areas, iii) quantified common carp

biomass dynamics with a SDBDM, iv) used the fitted SDBDM to identify harvest levels

required to achieve a mean biomass of ≤ 100 kg/ha for three alternative management

strategies over a 50 year period, v) evaluated sensitivity of management strategies to

unintentional under-harvest, and vi) evaluated EC as a tool to set harvest targets in sparse data situations.

METHODS

Study area.—Clear Lake is a 1474 ha shallow (mean depth = 2.9 m) glacial lake located in the western cornbelt plains ecoregion of north central Iowa (43°08’N, 93°22’W;

Figure 1). Lake economic value is approximately 43 million USD annually, with the majority of value associated with vacation and recreational use (CARD 2008). An open- water and ice fishery primarily targets walleye, yellow bass (Morone mississippiensis), and

black bullhead (Ameiurus melas), with an estimated value between 1 and 2.5 million USD

annually (S. Grummer, Iowa Department of Natural Resources (IADNR), unpublished data).

Water quality has declined over the last half century (Egertson et al. 2004), but has fluctuated

over the past 10 years with Secchi transparencies varying from 0.1 to 2.9 m and periodic cyanobacterial blooms during summer months (Iowa Lakes Information System 2005; Colvin

et al. 2010). Reduced water transparency has been associated with resuspended sediment and

organic material by increasing common carp biomass. Recycling of nutrients into the water

column by common carp promotes production of cyanobacteria and other phytoplankton in

this system (Downing et al. 2001; Wahl 2001). Water flows into the lake from Ventura

Marsh, a shallow 81 ha wetland (Figure 1). Metal grates prevent adult common carp from

moving between Clear Lake and Ventura Marsh. Lake aerators are used to limit winter-kill

events during ice-covered periods.

Commercial fishery.—A commercial fishery for common carp is used to limit impacts

of common carp on the Clear Lake ecosystem, with more than 1 million kg of common carp

harvested from the system over the past 70 years (Wahl 2001; Colvin et al. 2010). In

contrast to commercial fisheries for economically valuable species that occur over much

longer time periods, a pulsed commercial fishery operates for short periods of time (< 2

weeks) on Clear Lake. Pulsed harvests occur during the spawning season beginning in late

May or early June with an additional fall harvest period of similar duration occurring between late October and early November. A period of high common carp biomass (~540

kg/ha) occurring in the early 2000s corresponded with severely reduced water quality,

prompting IADNR to augment the commercial fishery with a 0.22 USD/kg subsidy in an

attempt to increase harvest (Wahl 2001; Larscheid 2005). Common carp biomass has

fluctuated over the past 10 years despite continued intense commercial fishing (Larscheid

2005; Colvin et al. 2010). However, identification of common carp aggregation areas in space and time (Penne and Pierce 2008) has led to more efficient fishing and an overall reduction in fishing effort over the past four years.

Common carp biomass estimation

Marking and recapture.—Annual common carp abundance was estimated by mark- recapture. Between 2,840 and 3,515 common carp were captured each year by commercial fisherman during spring harvest events from 2007 to 2010 and were marked with a year-

specific fin clip. Marked fish were released alive and allowed to mix with the unmarked

population over the summer, then recaptured during fall commercial harvest. The entire fall

harvest was sorted and marked and unmarked fish were enumerated. Individual lengths

(nearest 1 mm) and weights (nearest 200 g) were measured for a representative subset of

captured common carp. The common carp population was assumed to be closed to

immigration, emigration, and mortality during the summer mixing period.

Abundance estimation.—Annual common carp abundance was estimated using closed

population mark-recapture models. Common carp abundance was estimated by fitting

binomial, hyper-geometric, and multinomial (Mt) models to mark-recapture data by

maximum likelihood (Otis et al. 1978; White et al. 1982; Hayes et al. 2007). Annual

abundance estimates () were found by maximizing model-specific log likelihoods using a

quasi-Newton non-linear search algorithm (for specific likelihood functions for mark-

recapture models see Otis et al. 1978; Amstrup et al. 2005; Hayes et al. 2007) using the optim

function in R (R Development Core Team 2010). Variance of was calculated by solving

the inverse of the numerically derived Hessian matrix (Bolker 2008).

Model selection.—An information-theoretic approach was used to select between

competing mark-recapture models. Akaike's information criterion (AIC) was calculated for

each model using the maximized log likelihood and number of model parameters for each

year (Akaike 1974). Delta AIC (∆AIC) for each model was calculated as the within-year

difference of model-specific AIC and minimum AIC. Model-specific likelihood was

-0.5∆AIC calculated as e and relatively weighted (wmodel) to sum to 1 (Burnham and Anderson

2002). Common carp abundance estimates from the best approximating model or models

th within 1/10 of the maximum wmodel (i.e., wmodel >0.9) were used for subsequent annual

biomass estimates (Royall 1997).

Common carp biomass estimates.—Whole-lake biomass () was estimated by

multiplying annual mean common carp weight () at capture and . Variance of (V()) was calculated as: V() = •V •V V•V, where is mean

weight at capture, V is the variance of estimated abundance, is estimated abundance,

and V is variance of mean weight (Hayes et al. 2007). Ninety-five percent confidence intervals were calculated assuming was normally distributed. Estimates of and 95%

confidence intervals were divided by lake area (1464 ha) to standardize to area (i.e., kg/ha).

Date of initial capture was converted to the nearest 0.001 year for subsequent BDM fitting.

Biomass indices.—Common carp biomass was indexed by daytime (0800-1900 hrs)

trawling during September and October of 2007 to 2010. A semi-balloon otter trawl with an

8 m head rope, 3.8 cm stretch mesh body, and 6.3 mm mesh cod end was used to sample

common carp occupying the offshore zone of the lake. Trawling locations were allocated

proportionally to the area of the three lake basins, with starting location and trawling

direction selected at random. Routes were constrained to be greater than 100 m from shore

and 400 m from the nearest trawling location. Each trawling sample was conducted at a

speed of 3.2–4 km/hour for a period of 5 minutes to maintain comparability with previous

trawling efforts (Larscheid 2005). Captured common carp were enumerated, measured

(nearest 1 mm), and weighed (nearest 200 g). Catch per unit effort (I) was calculated as the

total common carp biomass captured per trawl sample. Annual mean CPUE () was

calculated as mean among-sample CPUE within year. Bootstrap resampling was used to

estimate variance and coefficient of variation (CV) of annual (Efron and Tibshirani 1991).

Median date of trawling was converted to the nearest 0.001 year for subsequent BDM fitting.

Biomass dynamics model.—An exponential semi-discrete biomass dynamics models

(SDBDM) was fit to annual and data for 2007 to 2010. SDBDMs extend continuous

BDMs to accommodate discrete harvest events (Colvin et al. in revision). An exponential model including I was used because only 4 years of data were available to fit the model, precluding the use of more complex BDMs which include parameters that limit production

(e.g., Schaefer, Fox). Because biomass as high as 540 kg/ha had been estimated as recently

as 2002 (Larscheid 2005), biomass estimated during 2007 to 2010 to be in the range of 100–

200 kg/ha was assumed to be well below carrying capacity. The exponential SDBDM used to model and dynamics was:

•, , (1) , •

where r is the intrinsic growth rate, B(t) is biomass at time t, B( ) is biomass

instantaneously after pulse harvest event at time τk, C() is biomass harvested during event

, ( t) is mean CPUE at time t, q is catchability, t is continuous time, τk is time of pulsed harvest event, and k indexes pulse harvest events. The SDBDM was solved by numerical integration for t = 2007 to 2011 using a timestep of 0.001. Livermore numerical integration routines are the most accurate and were used for all numerical integration using the deSolve package in R (Stevens 2009; Soetaert et al. 2010).

Data and model fitting.—Exponential SDBDM parameters were estimated by maximum likelihood. The log likelihood was modified to account for uncertainties by including year-specific variance estimates for and . The log likelihood of the model was calculated as:

σσ ℓ, ,,|, , σ, σ ∑

, (2) / •σ ∑ e ••

where r is the intrinsic growth rate, B0 is the initial biomass, q is the catchability, y(t)i is the mark-recapture biomass estimate at time t, σ is inter-year residual standard deviation of y(t)i,

is the SDBDM-predicted biomass at time t, σi is the year-specific standard deviation of

each biomass estimate, ( t)j is mean CPUE at time t, is the SDBDM predicted mean

CPUE at time t, and σj is the year-specific CV of CPUE. Model predicted biomass and mean

CPUE values corresponding to estimated values of and based on fractional year were extracted from the predicted time series to calculate the log likelihood. was assumed to be

multiplicative and log-normally distributed, therefore year-specific CV was used for σj.

Parameter estimates that maximize the log likelihood were obtained using a bounded non-

linear quasi-Newton search algorithm using the optim function in R (Stevens 2009; R

Development Core Team 2010). All parameters were constrained to be positive. Parameter

estimate variances were calculated by solving the inverse of the numerically derived Hessian

matrix (Bolker 2008). The fitted SDBDM was used for all subsequent simulations to evaluate common carp management strategies.

Simulating management strategies

Scenarios.—Alternative harvest strategies to reduce common carp biomass were

evaluated by simulation using the fitted SDBDM. Scenarios are model based

implementations of management strategies formalized as mathematical functions. Hereafter

the term “scenario” will refer to management strategies simulated using the fitted SDBDM.

Scenario 1 evaluated continued use of seasonally pulsed commercial fishing as currently

practiced. Scenarios 2 and 3 evaluated biomanipulation management strategies where carp

biomass was significantly reduced when biomass exceeds a nuisance biomass level (Bnuisance)

ranging from 220 to 550 kg/ha (Table 1). Bnuisance was bounded between 220 and 550 kg/ha

so biomanipulation events did not occur too frequently (i.e., ever year) and to be the maximum of recent biomass estimates. Scenario 2 used a pulsed proportional reduction of

0.75 (Perrow et al. 1995; Hansson et al. 1998) and scenario 3 used a pulsed reduction of biomass to 100 kg/ha (Mehner et al. 2004b; Bajer et al. 2009) when biomass exceeded

Bnuisance. Post-biomanipulation fishing mortality (Fsupplemental) was also evaluated in scenarios

2 and 3. See Table 1 for mathematical formalizations of scenarios 1-3 used in simulations.

Simulation.—Common carp biomass was forecasted using the fitted SDBDM to evaluate harvest scenarios. A 50-year time period was used because current Iowa lake restoration legislation (HF2782, Section 26; 2006) requires restoration efforts to be sustainable over this time period. Parameters evaluated included Fspring, Ffall, Bnuisance, and

Fsupplemental for harvest functions of scenarios in Table 1. All F parameters (i.e., Fspring, Ffall,

Fsupplemental) were evaluated from 0.00 to 0.30 by increments of 0.01. Values of F were limited to 0.30 to reflect the maximum F observed for this population. Bnuisance was evaluated over the interval of 218 to 540 kg/ha by increments of 10. The lower limit of Bnuisance was set as the biomass after a three year period assuming initial biomass was 100 kg/ha. Spring harvest pulses occurred every year at 0.2 of each year and fall pulses at every 0.8 of each year. All harvest pulses in biomanipulation scenarios (scenarios 2 and 3) occurred every year at 0.2 of each year, simulating annual harvest pulses observed for Clear Lake. Every

parameter combination for each scenario in Table 1 was evaluated to identify a set of

parameter combinations resulting in a mean biomass ≤ 100 kg/ha over the simulation period.

In total 961 parameter combinations were evaluated for scenario 1 and 1023 combinations

were evaluated for scenarios 2 and 3.

Effect of unintentional under-harvest.—The effect of unintentional under-harvest on

management strategies was evaluated by simulation. A mean simulation biomass ≤ 100

kg/ha was achieved by 602, 192, and 187 parameter combinations for scenarios 1-3

respectively. Therefore a single set of parameter values were selected for each scenario and

used to evaluate the effect of unintentional under-harvest on biomass dynamics. Scenario 1

was evaluated using Fspring = 0.22 and Ffall =0.03 which replicates patterns in recent Clear

Lake harvest where 86% and 14% of Fannual occur in the spring and fall respectively (Colvin

et al. in revision). Mean spring harvest was Fspring=0.245 for 2007–2010 and Bnuisance was evaluated using values of 335.2 and 335.5 for scenario 2 and 3. Values of Bnuisance correspond

to the highest level of Bnuisance and Fspring of 0.245 from the solution parameter set for

scenarios 2 and 3. Selecting a high level of Bnuisance was used to minimize the number of

large-scale removals over time.

Under-harvest was simulated by randomly reducing fishing mortality of all three

scenarios. Under-harvest F values were calculated as: Funder = Fi•(1–ε), where, Fi is the

scenario-specific fishing mortalities (i.e., Fspring, Ffall, Fbiomanipulation, Fsupplemental), ε is a random

uniformly distributed value between 0 and η, with η as the upper bound of under-harvest.

Fbiomanipulation was 0.75 for scenario 2 and calculated as (B()-100)/B(t) for scenario 3.

Stochastic simulation was used to evaluate forecasted biomass dynamics for 100 replicates of

η equal to 0.1, 0.2, and 0.3. Values of η were selected to represent a reasonable range of

under-harvest (i.e., 10 to 30%). Effects of η on biomass dynamics were graphically assessed.

Number of biomanipulation pulses was summarized for each level of η for scenarios 2 and 3

to assess the effect of η on frequency of biomanipulation pulses.

Calculating and evaluating EC

Annual population summary.—Annual values were summarized for the common carp

population to calculate EC. Annual fishery harvest (C) was calculated as the annual sum of

common carp biomass harvested. Maximum individual weight in kg (wmax) was identified as

the maximum weight of individuals captured during spring marking, fall trawling or fall

recapture efforts for each year. Mean annual biomass in kg/ha () was calculated as the

annual mean of model-estimated biomass from the fitted SDBDM. Annual production (P) in

-1 -1 kg ha •yr was estimated as log10P=0.32+0.94•log10()-0.17•log10(wmax), where is the

mean standing biomass in kg/ha and wmax is the maximum individual weight observed

(Downing and Plante 1993). Annual fishing mortality Fannual was calculated as within-year sum of /, where is harvest at time t, and is model estimated biomass at

time t, for each time step. Year-specific production-to-biomass ratios were calculated as

P/. EC was calculated as C/P (Ricker 1946).

Evaluating the ecotrophic coefficient.—EC was evaluated as a potential tool for setting harvest targets when data are sparse by simulating biomass dynamics using the fitted

SDBDM over a 10-year period. Mean among-year P/ was used to calculate production and

harvest level for EC values varying from 0 to 1.3 by increments of 0.01. Values of EC

evaluated reflect observed EC values for this population in Clear Lake. Harvest amounts

were calculated as C(τk) = EC•P/•B(τk), where C(τk) is the amount of harvest

instantaneously removed at pulse time τk, EC is the ecotrophic coefficient, P/ is the mean

among-year production-to-biomass ratio, and B(τk) is the biomass at time t. Pulsed harvest

events occurred every year at 0.2 of the year. Instantaneous rate of change (γ) for annual

mean biomass (B) was calculated as γ = loge(/) for years nine and 10 of the

simulation to identify EC values resulting in decreasing (γ < 1) biomass over time.

RESULTS

Biomass and CPUE.—Common carp abundance and mean weight were variable over

the study period. Mean weight of common carp captured during spring marking efforts

varied from 3.8 to 5.7 kg (Table 2). A mark-recapture model that allowed for heterogeneous

capture efficiency (Mt) best approximated the data for all study years (wmodel>0.9) (Table 3).

Annual abundance estimates from model Mt varied from 35,738 (95% C.I.= 29,756–41,694) individuals in 2007 to 62,003 (95% C.I.= 54,133–69,872) in 2010. Carp biomass estimates

varied from 93 (95% C.I. = 86–99) kg/ha in 2007 to 233 (95% C.I. = 203–263) kg/ha in 2010

(Table 2). Annual biomass estimates have been variable over the study period (Figure 2).

Mean CPUE varied from 7.57 (95% C.I.= 4.9-12.6) to 9.89 (95% C.I.= 6.6-15.8) kg/trawl

(Table 4) over the same period (Figure 2).

Commercial harvest.—Commercial fishery harvest was variable over the study period

with largest within-year harvests occurring in the spring. Annual harvest varied from 56 to

8.8 kg/ha from 2007 to 2010, decreasing over time (Table 4; Figure 2). Spring harvest was typically higher than fall harvest (mean harvest: Spring = 30 kg/ha, Fall = 5.7 kg/ha) (Figure

2). Spring and fall harvest varied from 0.39 to 51.5 kg/ha and 2.58 to 8.45 kg/ha respectively

(Table 4).

SDBDM.—Visual inspection of model predictions indicate that the SDBDM fit biomass and data well (Figure 3). Estimated intrinsic growth rate (r), and catchability (q)

were 0.27 (95% C.I. = 0.18–0.36) and 0.06 (95% C.I. = 0.041–0.070), respectively. Initial

biomass (B0) was estimated to be 142 kg/ha (95% C.I. = 102–182). Time required to double

biomass was 2.7 years. Simulated termination of commercial harvest resulted in common

carp biomass exceeding the previous maximum biomass estimate of 540 kg/ha by the year

2014.

Evaluating management scenarios

Scenario 1.—Mean simulated biomass varied with Fspring and Ffall for scenario 1. A

narrow range of Fannual (Fspring + Ffall) varying from 0.244 to 0.265 was needed to achieve

biomass objectives. Increased Fannual was required when Fspring and Ffall were similar in size

(e.g., Fspring = 0.12 and Ffall = 0.14) to achieve biomass objectives. Lower Fannual was

required to achieve biomass objectives when Fspring and Ffall were high relative to each other

(e.g., Fspring = 0.24 and Ffall = 0, Fspring = 0 and Ffall = 0.25) (Figure 4). Based on adequate

harvest (i.e., no under-harvest) spring and fall harvest targets over 10 years decreased from

33 to 32 kg/ha and 4.1 to 3.9 kg/ha, respectively to achieve biomass objectives (Table 5).

Over the first 10 years of the simulation spring and fall harvest targets totaled 314 and 39

kg/ha to achieve biomass objectives (Table 5). Common carp biomass increased quickly for

all levels of η, indicating that under-harvest in either spring or fall can have a strong effect on

common carp biomass dynamics (Figure 5).

Scenario 2.—Mean simulated biomass varied with Bnuisance and Fsupplemental for scenario

2. Biomass objectives could not be achieved without Fsupplemental values exceeding 0.227.

Increasing Fsupplemental was needed with increasing Bnuisance to reach biomass objectives (Figure

4). Scenario 2 was relatively insensitive to level of η, with slight biomass increases for η

values of 0.1–0.3 (Figure 5). Mean number of biomanipulation pulses increased with

increasing η varying from 1.02 for η = 0.1 to 1.56 for η = 0.3 (Table 6). Harvest targets based on the fitted SDBDM evaluated at Bnuisance=335.5 kg/ha and Fsupplemental=0.245 required

no harvest in the first four years and an initial biomass reduction of 328.5 kg/ha when

Bnuisance was exceeded, followed by supplemental harvest of 35.1 kg/ha in the following year

(Table 5). A total of 499 kg/ha of harvest was required over the initial ten-year period to

achieve biomass objectives. Scenario 2 was relatively insensitive to level of η, with slight

increases in biomass for all η values (Figure 5). Mean number of biomanipulation pulses

increased with η, varying from 1 to 2.7 for η levels of 0.1 and 0.3 (Table 6).

Scenario 3.—Mean simulated biomass varied by Bnuisance and Fsupplemental for scenario

3. Biomass objectives could not be achieved without additional Fsupplemental exceeding 0.241.

Increasing Fsupplemental was required with increasing values of Bnuisance to achieve biomass objectives (Figure 4). Harvest targets based on the fitted SDBDM evaluated at Bnuisance=335 kg/ha and Fsupplemental=0.25 required no harvest in the first four years and an initial biomass

reduction of 329 kg/ha followed by supplemental harvest of 35 kg/ha in the following year

(Table 5). A total of 494 kg/ha of harvest was required over the initial 10 year period to

achieve biomass objectives. Scenario 3 was relatively insensitive to level of η, with slight

increases in biomass for all η values (Figure 5). Mean number of biomanipulation pulses

increased from 1 to 2.84 for η levels of 0.1 and 0.3 (Table 6).

Ecotrophic coefficient (EC)

Maximum individual common carp weights (wmax) varied from 10.0 to 16.0 kg over

the study period (Table 4). Mean annual biomass () varied from 138 to 169 kg/ha. Fishing

mortality (F) varied from 0.05 to 0.48. Production estimates varied from 38.1 to 48.6 kg ha-1 yr-1. P/ values were consistent among years, varying from 0.296 to 0.323. EC varied from

1.34 to 0.18, decreasing over the study period (Table 4). Harvesting a majority of annual

production (EC=0.76) was required to achieve no change in population biomass based on model simulations. Simulated harvesting at EC > 0.76 resulted in biomass declines.

DISCUSSION

Formal stock assessments of common carp in North America are rare despite the widespread distribution and the fact that commercial fisheries are commonly used to control their biomass. This study contributes to the relatively few published common carp stock assessments (Li and Mosman 1977; Linfield 1980; Pinto et al. 2005). Common carp biomass was found to be increasing in our study system, while harvest has been variable with sufficiently high commercial fishing mortality (i.e., Fannual>r) only occurring in 2007 and

2008 of our four year study. Biomass doubling time was estimated to be 2.7 years based on

r=0.27, which is less than current 3-year commercial fishery contracts utilized by the state of

Iowa. Contract commercial fishing can play a key role in controlling common carp, but

consecutive years of under-harvest may result in doubling of biomass during the contract

period. The contract bidding process can result in different commercial fishers working in

different time periods, each with varying efficiencies and system-specific knowledge, all of

which may limit efficacy of commercial fishing to control common carp biomass over time.

The possibility that harvest amounts could vary over time, the apparent sensitivity of

common carp biomass dynamics to unintentional under-harvest, and the relatively short

doubling time we documented in Clear Lake may go a long way toward explaining why

commercial fisheries in inland waters have rarely succeeded in long-term carp biomass

control (Wydoski and Wiley 1999).

Analysis assumptions

Recruitment and migration.—Our mark-recapture analysis assumed the population was closed to recruitment and migration. Recruitment is believed to be negligible in Clear

Lake given the lack of small carp (<270 mm) captured in yearly fall index seining (S.

Grummer IADNR, unpublished data). Migration of juvenile common carp from Ventura

Marsh through the exclusion barrier is believed to be the primary recruitment source for

Clear Lake, which potentially violates the assumption of population closure. However,

juvenile immigrants are typically too small to be captured by commercial fishery gear in the

year they enter the lake, thus they had no effect on our abundance estimates. If immigrant

juvenile carp are captured in commercial fishing gear, abundance would be overestimated,

positively biasing biomass estimates. Systematic overestimation of biomass should result in

an overestimate initial biomass (B0) which in turn would positively bias harvest targets.

Mortality.—Mortality (i.e., predation, senescence, other) over the mark-recapture

period can negatively bias abundance estimates by decreasing the number of fish captured

during recapture efforts. Common carp can exceed 300 mm by age 2 (Larscheid 2005;

Colvin et al. 2010), rapidly exceeding gape size of the majority of piscivorous predators in

the lake (Sammons et al. 1994). Common carp can be found in littoral areas of the lake

during the summer (Penne and Pierce 2008) and may be susceptible to avian predation.

Large avian predators capable of preying on common carp (e.g., bald eagles Haliaeetus

leucocephalus, white pelicans Pelecanus erythrorhynchos) are present and may pose

predation risk (Knopf and Kennedy 1981; Findholt and Anderson 1995). However, white

pelicans generally occupy the adjacent Ventura Marsh (M. Colvin personal observation), and

predation by piscivorous birds is likely limited due to the large size of the fish (mean length

~ 603 mm) and low water transparency. Common carp can tolerate low dissolved oxygen

levels and are resistant to summer- and winter-kills (Edwards and Twomey 1982; Panek

1987). Early studies of common carp dynamics found mean monthly mortality rates of

0.06% (over 4 months) and 0.045% (over 19 months) with mortality rates lower in summer than winter (Neess et al. 1957). Common carp mortality over the mark-recapture mixing period should be minimal or nonexistent; however if mortality did occur abundance would be underestimated.

Biomass dynamics.—Biomass dynamic models require several assumptions to approximate true biomass dynamics. Water temperature is a major factor affecting production (Jobling 1994). The assumption that r is constant over time (i.e., biomass production does not vary with seasonal changes in temperature) may bias parameters

estimated for BDMs since it does not incorporate variation in biomass production due to thermal variability. Maximum water temperature in Clear Lake is approximately 30C in the summer (M. Colvin unpublished data) which is also the thermal optimum for growth of common carp (Goolish and Adelman 1984). However, water temperature is below 30C the majority of the year and ice covers the lake from December until late March or early April

(Jacobsen 1968; Penne and Pierce 2008). Incorporating temperature would likely result in increased biomass production (i.e., higher r) when water temperatures are warmest. The fitted SDBDM applies a constant r over an annual period which ignores temperature-related variation in biomass production, but based on the data fit we believe our analysis provides predictions and fit adequate to set harvest targets.

Fitting the model required an assumption that CPUE is proportional to biomass (i.e., constant q) and production is density-independent. Assuming CPUE is proportional to B is a common assumption for inland fish species and the basis for several published studies

relating common carp to water quality (e.g., Egertson and Downing 2004; Jackson et al.

2010; Weber et al. 2010). Whether common carp catchability (q) exhibits hyper-stability

(i.e., high catches at low abundance) or hyper-depletion (i.e., low catches at high abundance)

behavior is unknown and should be further examined (Harley et al. 2001). The SDBDM

assumes production is density-independent which overestimates production at high

biomasses. Ideally, production should be limited at high biomass, as is the case for models

that include a carrying capacity term (e.g., logistic; Schaefer 1954). However, given our

limited data and low biomass levels relative to a previous maximum biomass estimate (540

kg/ha; Larscheid 2005) we believe it is reasonable to fit an exponential BDM. As more data

are accumulated, more complex models can be fit that account for density-dependent

production constraints (e.g., Schaefer, Fox), constant q, and model uncertainty.

Assimilating data from multiple sources is often necessary to make informed management decisions. Including CPUE as a biomass index, with q simultaneously

estimated with the exponential SDBDM parameters provides managers a tool to make

informed decisions in absence of annual biomass estimates. Mark-recapture abundance

estimates are invaluable for estimating biomass and fitting models, but mark-recapture

studies are not always accepted by the public. Public perception of returning large numbers of live common carp back into the ecosystem to cause further environmental degradation can potentially cause a public-relations backlash. BDMs utilizing CPUE provides managers with a tool to utilize CPUE data in a way that can potentially the need for annual mark-recapture

population estimates. Essentially, CPUE data can be used to supplement BDMs with

biomass indexes in years when mark-recapture studies are not conducted.

Management scenarios

Model simulations indicate that biomanipulation (scenarios 2 and 3) never achieve

biomass objectives unless supplemental fishing mortality is imposed. The majority of

biomanipulation projects where common carp biomass was proportionally reduced by 0.75 or

more were unsuccessful in the long term (Meijer et al. 1998) and this was consistent with our

simulation results. For biomanipulation to be successful without supplemental fishing

mortality, a reduction of r is required. However, biological processes (e.g., predation)

necessary to reduce r may be delayed until the fish assemblage responds to biomanipulation

and subsequent change in water quality. In the post-biomanipulation period common carp

production likely remains unchanged, allowing common carp to potentially dominate the fish

assemblage again. Fish assemblage changes following biomanipulation may take several

years (Rose and Moen 1953) and potentially result in delayed predation on common carp by

native piscivores, thus requiring supplemental fishing mortality to compensate for this delay.

Predation mortality can be a significant mechanism regulating biomass dynamics if increases

in piscivorous fishes occur following biomanipulation. In disturbance limited systems (e.g.,

winter-kills), native centrarchids can prey heavily on common carp eggs, potentially

regulating common carp populations (P. Bajer U. Minn., personal communication).

Scenarios 2 and 3 were relatively robust to under-harvest. Setting a system nuisance

biomass threshold (Bnuisance) simulates an active management feedback. To evaluate Bnuisance, a manager needs to know the biomass to make a management decision. When under-harvest occurs, the manager can respond with appropriate biomass reductions. Feedback provided to the manager by evaluating Bnuisance is important as common carp biomass can rapidly increase

with only a few years of under-harvest (i.e., biomass can double in~2.7 years). Management in light of likely under-harvest is difficult; however continued monitoring through biomass

estimates or calibrated CPUE can provide managers with information on population biomass

and whether or not management actions may be required. Additionally a precautionary approach can be used where harvest targets are arbitrarily increased to compensate for the

possibility of under-harvest.

Annual metrics and EC

Common carp P/ calculated from production estimates and mean biomass was

greater than r. P/ is expected to be slightly higher than r when predation and other

mortality are small components of annual production (i.e., high surplus production). This is

supported by our analysis where r was approximately 83% of mean P/. Fast growing

common carp escape predation by rapidly exceeding predator gape limitation (Sammons et

al. 1994; Ward et al. 2008). Additionally, the long life span of common carp in Clear Lake

(Maximum age=13; Colvin et al. 2010) indicate that other mortality (e.g., senescence) is a

small component of production.

EC provided a simple production-based method to set biomass harvest targets based

on data that can be collected within a single year. The EC value required to cause declines in

common carp biomass was higher than the value of 0.5 recommended for salmonids in

Minnesota (Waters 1992). Common carp are generally less productive in terms of biomass

turnover (i.e., lower P/B) than salmonid stocks due to their long life span (Carlander 1969;

Colvin et al. 2010), however rapid growth and low predation rates result in increased production relative to salmonids. We expected that EC should be higher than the guidelines presented in Waters (1992) because the majority of annual production is surplus in common

carp. Harvest targets can be based on data from a single year by multiplying estimated

annual production by EC. Monitoring in the following year can determine whether or not

biomass removals were sufficient to decrease biomass and EC can then be adjusted

accordingly in an adaptive management framework (Walters 1986). EC levels established in

our analysis (EC≥0.76) can provide a management tool to determine harvest targets for

nuisance common carp in sparse-data situations. Additionally, EC may be used to establish

or to provide supplemental harvest targets in existing common carp commercial fisheries

lacking time series data.

Management implications

Managers seeking to reduce common carp biomass choose among several strategies.

They can take a wait-and-see approach, collect more data, or embrace uncertainty in an

adaptive management framework (Walters 1986; Starfield 1997). Doing nothing would

likely lead to common carp biomass returning to previous nuisance levels (~540 kg/ha) in

just a few years. In some studies delaying conservation or restoration efforts to collect

additional data beyond 2 years did not increase the ultimate effectiveness of restoration

efforts (Grantham et al. 2009). Tools like BDMs and EC can be used to set annual harvest

targets that can be revised each year as more data are accumulated in an adaptive

management framework (Walters 1986). Singular biomanipulation events are likely to be

unsuccessful over the long-term unless common carp production is reduced through changes

in biological processes (i.e., increased predation) or supplemental harvest is utilized to

compensate for delays in biological processes following biomanipulation.

SDBDMs are a flexible approach for utilizing a variety of data to establish the harvest

targets required to control nuisance populations through commercial fishing. Overall, BDMs

require simpler data than existing age-structured approaches (e.g., Brown and Walker 2004;

Weber et al. 2011). While age-structured models (e.g., yield per recruit, dynamic pool) are

potentially useful, harvest targets cannot be directly set without biomass knowledge. Ideally,

a combination of biomass and age structure models would be used to develop harvest targets

for nuisance common carp. However, obtaining both catch and age structure data is a tall

order for most inland fisheries— especially nuisance fisheries. Because of the realities of

ever-increasing limitation on personnel and budgets, biomass-based frameworks are likely to

be the only realistic approach to identify commercial harvest targets to control common carp.

ACKNOWLEDGMENTS

We thank IADNR for providing common carp harvest data. We thank Dr. Chris

Chizinski for critical comments on previous drafts of this manuscript. This project was

supported in part by the Department of Natural Resource Ecology and Management at Iowa

State University, Iowa Cooperative Fish and Wildlife Research Unit, and Iowa Department of Natural Resources. The use of trade names or products does not constitute endorsement by the U.S. Government.

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4780000

Ventura 4778000 Clear Lake Marsh

4776000

Northing 4774000

4772000

4770000 N 2 km

460000 462000 464000 466000 468000 Easting

Figure 1. Clear Lake, north central Iowa. Lake inflow travels east from Ventura Marsh. Adult carp are prevented from moving between Clear Lake and Ventura Marsh by a carp exclusion device located at the inlet to the lake.

250 A: Biomass 200 150 100

Biomass (kg/ha) 50 0

16 B: CPUE 14 12 10 8 Catch per unit effort (kg/trawl) effort 6 4

C: Weekly harvest 25 April-June October-November 20 15 10

Biomass (kg/ha) 5 0 2007 2008 2009 2010 2011 Year

Figure 2. Common carp biomass, catch per unit effort (CPUE), and commercial harvest over a four year period in Clear Lake, IA. Vertical lines in panels A and B represent 95% confidence intervals.

250

200

150 Biomass (kg/ha) Biomass

100

Catch per unit unit per Catch 8 effort (CPUE: kg/trawl) 6

4 2007 2008 2009 2010 2011

Figure 3. Observed and predicted common carp biomass (top panel) and trawling catch per unit effort (bottom panel) over a four year period in Clear Lake, Iowa. Predicted biomass and CPUE are from the fitted SDBDM and denoted by the dotted line. Vertical lines denote 95% confidence intervals.

93 ) fall 0.30 A: Scenario 1 0.25 0.20 Objective Objective 0.15 not met met 0.10 0.05 0.00 Fall fishing mortality (F 500 B: Scenario 2 450 ) 400 Objective Objective 350 not met met nuisance 300 250

500 C: Scenario 3 450 400 Objective Objective not met met

Nuisance biomass (B (B biomass Nuisance 350 300 250

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Spring fishing mortality (Fspring)

Figure 4. Plot of parameter combinations evaluated for scenarios 1-3 (Panels A-C). Every combination of x-axis and y-axis parameters was used to calculate mean 50-year simulation biomass. The grey area highlights parameter combinations where the mean simulation biomasses were ≤ 100 kg/ha. The black dots in each panel denote the values of parameter combinations used to evaluate the potential consequences of unintentional under-harvest.

η = 0.1 η = 0.2 η = 0.3 250 A 600 B C 1200 500 200 1000 400 150 800 300 600 100 200 400 50 100 200

0 0 0

D E F 400 400 400

300 300 300

200 200 200

100 100 100 Bioamss (kg/ha)

0 0 0

G H I 400 400 400

300 300 300

200 200 200

100 100 100

0 0 0

2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060 Year Figure 5. Effect of varying levels of under-harvest on common carp biomass dynamics. Black lines denote biomass dynamics for baselin harvest with no under-harvest and are shown for reference. Dark grey lines denote mean biomass assuming under-harvest and the grey area denotes simluation envelopes (i.e., simulation bounds) of 100 replicate stochastic simualations for scenario 1 (Panels A–C), scenario 2 (Panels D–F), and scenario 3 (Panels G–I). Panels in the left column (η=0.1) display results for mild under-harvest, panels in the middle column (η=0.2) for moderate under-harvest, and panels in the right column (η=0.3) for severe under-harvest.

Table 1. Management scenarios and parameter intervals evaluated using the fitted SDBDM. F type Harvest functions Management parameter intervals

Scenario 1: •,, Fspring = [0.00, 0.3] Commercial F = [0.00, 0.3] •,, fall harvest Scenario 2: 0, 3 Bnuisance = [218, 540] 0.75 • , , 3, Proportional , Fsupplemental = [0.00, 0.3] reduction by 0.75a •, , 3,,

Scenario 3: 0, 3 Bnuisance = [218, 540] 100, , 3, Biomass reduction , Fsupplemental = [0.00, 0.3] to 100 kg/ha b •, , 3,, τk,spring = {0.2,1.2,…50.2} τk,fall = {0.8,1.8,…50.8} a Perrow et al. (1995), Hansson et al. (1998) 95 b Bajer et al. (2009)

Table 2. Summary of carp mark-recapture efforts, mean weight at capture, catch per unit effort (CPUE), and estimated biomass. Weight (kg) CPUE (kg/trawl) Biomass (kg/ha) Year Marked Captured Recaptured Mean Std. Dev. Mean Std. Dev. Estimate Std. Dev. 2007 3515 1387 136 3.8 1.20 7.82 0.0966 92.6 8.50 2008 2959 1702 89 5.2 1.88 7.74 0.0976 211.9 28.86 2009 3367 819 76 5.7 1.82 7.57 0.0999 126.9 15.06 2010 2840 2450 172 5.5 1.59 9.98 0.0757 159.5 10.13

Table 3. Model selection of mark recapture models assuming a closed population fit by maximum likelihood. There was substantial support for a model that allowed for heterogeneous capture probabilities (Mt) with a relative model weight wmodel > 0.9. Year Model s. e. AIC ∆AIC l(model) wmodel 2007 Multinomial (Mt) 35725 3045 -64553 0.00 1 1 Bailey's binomial 358482919 -31111 3341.93 0 0 Hypergeometric 35848 2499 3107 67660.26 0 0 2008 Multinomial (Mt) 59330 4723 -61178 0.00 1 1 Bailey's binomial 565875816 -18135 43043.16 0 0 Hypergeometric 56587 4144 2121 63298.37 0 0 2009 Multinomial (Mt) 32615 3432 -54566 0.00 1 1 Bailey's binomial 329173618 -38617 15949 0 0 Hypergeometric 32917 2931 1725 56292 0 0 2010 Multinomial (Mt) 62003 4015 -76957 0.00 1 1 Bailey's binomial 619485251 -7240 69717 0 0 Hypergeometric 61948 5862 3167 80124 0 0

Table 4. Estimates of mean annual common carp standing biomass (), annual commercial fishery harvest (C), fishing mortality (F), production (P), production to biomass ratio (⁄ ) and ecotrophic coefficient (EC). Mean annual biomass in kg/ha was calculated as the annual mean of model-estimated biomass. Year C wmax F P ⁄ EC 2007 56.06 10.0 152.3 0.368 42.8 0.323 1.14 2008 58.05 12.8 138.7 0.419 38.1 0.311 1.34 2009 19.54 12.8 138.9 0.141 39.4 0.311 0.45 2010 8.82 16.0 169.3 0.052 48.6 0.296 0.18 Mean 35.62 12.9 149.8 0.245 42.2 0.311 0.78

Table 5. Harvest targets required to achieve biomass objectives for harvest scenarios evaluated. Harvest amounts calculated using the parameter combinations for scenarios 1-3 highlighted by black dots in Figure 4. Harvest (kg/ha) Scenario 1 Scenario 2 Scenario 3 Year Spring Fall Spring Spring 2011 33.0 4.1 0 0 2012 32.6 4.1 0 0 2013 32.3 4.0 0 0 2014 31.9 4.0 0 0 2015 31.6 3.9 328.5 338.0 2016 31.2 3.9 35.1 32.0 2017 30.9 3.9 34.6 31.6 2018 30.6 3.8 34.2 31.2 2019 30.2 3.8 33.7 30.8 2020 29.9 3.7 33.3 30.4 Total 314.2 39.2 499.4 494

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Table 6. Scenario-specific summary of 100 replicate stochastic simulations of scenario 2 and 3 for varying levels of under-harvest (η) and biomanipulation. Biomanipulation events Scenario η Mean Median Range Std. dev. 2 0.1 1.00 1 [1,1] 0.00 0.2 1.86 2 [1,2] 0.35 0.3 2.72 3 [2,4] 0.55 3 0.2 1.0 1 [1,1] 0.00 0.3 1.68 2 [1,2] 0.46 0.4 2.84 3 [2,4] 0.52

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CHAPTER 4: COMMON CARP, ZEBRA MUSSELS, AND THE FOOD WEB:

CONSEQUENCES OF NON-NATIVE SPECIES INVASIONS FOR LAKE

RESTORATION AND RECREATIONAL FISHERIES

ABSTRACT

Lakes and resources they provide can be of significant local economic importance.

However, the value of such systems is compromised by effects of non-native species invasions. Recreational fisheries provide significant economic contributions to local economies, however pelagic phytoplankton reduction by non-native zebra mussel grazing may limit trophic support for sport fishes, thereby limiting recreational fishery yield. Four annual mass-balance food web models were developed to evaluate food web consumption, group impacts and structuring, as well as pelagic primary production in a shallow eutrophic lake. Total system consumption was dominated by benthic groups including chironomids, and exotic common carp and zebra mussels. However, common carp and zebra mussel food web impacts were limited to benthic invertebrates and phytoplankton respectively. Zebra mussels and zooplankton had the largest negative impact on pelagic phytoplankton, and age 0 yellow bass had a negative impact on zooplankton. Chironomids, yellow bass, and walleye had positive impacts on the recreational fishery. Impacts of the remaining food web groups on the recreational fishery were limited, indicating that walleye, yellow bass, and the recreational fishery may be a subsystem supported by chironomid and age 0 yellow bass production. Keystone analysis indicated that clear water and turbid water adapted groups structured the food web among study years, suggesting that the lake may be in a transitional water quality state. Zooplankton consumption by age 0 yellow bass was 12 to 47% of annual zooplankton production and limited top down control of phytoplankton. Zebra mussels

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occupy a similar trophic niche as zooplankton and are likely compensating for reduced

zooplankton abundance, potentially leading to improved water clarity and successful lake

restoration. Impacts of common carp and zebra mussels on the recreational fishery were

limited; however this was likely due to the limited connection of dominant sport fish to

pelagic food webs.

KEYWORDS

ECOPATH, non–native species, zebra mussels Dreissena polymorpha, common carp

Cyprinus carpio, shallow lake restoration, trophic interactions

INTRODUCTION

Lakes provide significant local economic value (CARD 2008) but are at risk to non-

native species invasion (e.g., Drake and Bossenbroek 2004; Whittier et al. 2007; Johnson et al. 2008; Vander Zanden and Olden 2008). Economic losses associated with invasions are attributed to remediation costs and loss of recreational income (Pimentel et al. 2000;

Pimentel et al. 2001). However, effects of non-native species on economically valuable recreational fisheries may also be important. Non-native species can alter food webs, potentially limiting energy available to sport fish occupying higher trophic levels (Simon and

Townsend 2003; Miehls et al. 2009) and mediating trophic interactions by providing new

prey habitat and foraging opportunities (Stewart et al. 1998; Idrisi et al. 2001; Gergs et al.

2009). Previous studies have focused on the effects of non-native species on ecosystem level metrics such as biodiversity, community composition, and species abundance (McCann

2007; Nilsson et al. 2011); however these changes are, at least in part, due to modified food web structure and trophic flows. In particular, non-native species consume resources that would otherwise be utilized by native consumers. Managing and minimizing economic and

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ecosystem impacts of future invasions will require understanding non-native species impacts

within food webs.

Non-native common carp (Cyprinus carpio) are distributed throughout aquatic systems of the United States and the rest of the world. High common carp biomass has been

associated with reduced population sizes of indigenous species, reduced taxonomic diversity, composition, and altered community composition. For example, aquatic systems with high common carp biomass exhibit reduced macrophyte, phytoplankton, and macroinvertebrate diversity (Lougheed et al. 1998; Chumchal et al. 2005; Miller and Crowl 2006). Common

carp can also dominate fish assemblages, reducing species diversity and abundance (Cahn

1929; Weber and Brown 2009; Weber and Brown 2011). Aquatic macrophytes anchor the

benthos in shallow lakes, limiting benthic resuspension which is important in maintaining a clear water state in shallow lakes (Scheffer 1998; Chakrabarty and Das 2007; Matsuzaki et al. 2007; Roozen et al. 2007). Common carp are benthic omnivores that promote a turbid- water state in shallow lake ecosystems by uprooting aquatic macrophytes, resuspending sediments and nutrients, and nutrient recycling (Crivelli 1983; Schrage and Downing 2004;

Chumchal et al. 2005; Driver et al. 2005). Understanding the role common carp play in structuring aquatic communities via strictly trophic interactions is limited for lake ecosystems, since previous research has focused on experimental mesocosms (e.g., Parkos et al. 2003), carp exclosures within lakes (e.g., Lougheed et al. 1998), and large scale experimental biomass reductions (e.g., Schrage and Downing 2004).

Eutrophication of lakes is a global phenomenon, where excess nutrient inputs lead to degraded water quality (Kleinman and Sharpley 2001; Bronmark and Hansson 2002; Phillips

2005). Excess nutrients increase pelagic phytoplankton production, which decreases water

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transparency and can result in cyanobacterial dominance of phytoplankton assemblages

(Downing et al. 2001b; Kalff 2003). These conditions are typical of lake ecosystems of the

Midwestern United States, which have experienced water quality declines over time, particularly in landscapes dominated by intensive agriculture (EPA 2000). Economic value of multiple-use Midwestern lakes is dependent on adequate water quality (CARD 2008). The demonstrated value of improved water quality in these systems has led to significant efforts to restore and improve lake water quality by reducing external nutrient loading.

Lake restoration strategies are not limited to managing external nutrient loading.

Once control of external nutrient loading is achieved, restoration efforts turn to limiting nutrient recycling within the lake. Common carp removal is commonly used to reduce benthic resuspension and cycling of nutrients (i.e., internal loading) thereby improving water quality (Schrage and Downing 2004). However, long term success of lake restoration can be hindered by increased zooplanktivorous fish (e.g. age 0 yellow perch, Perca flavescens) production that typically occurs following lake restoration (Sondergaard et al. 2007).

Zooplanktivorous fish indirectly affect water quality by limiting top down control of phytoplankton by zooplankton, a process widely known as a trophic cascade (Lathrop et al.

2002). Understanding direct and indirect impacts of consumers on phytoplankton and zooplankton production may increase the likelihood of successful lake restoration, especially if consumers exerting a negative impact on zooplankton within the food web can be identified.

Zebra mussels (Dreissena polymorpha) are recent invaders of North American inland aquatic systems. These filter-feeding organisms directly reduce phytoplankton abundance through herbivory (MacIsaac et al. 1992; Karatayev et al. 1997; MacIsaac et al. 1999) and

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alter food web structure (Richardson and Bartsch 1997; Zhu et al. 2006; Miehls et al. 2009).

At high densities, zebra mussel herbivory can increase water clarity, resulting in increased

abundance and distribution of aquatic macrophytes (Zhu et al. 2006) and benthic algae

abundance (Mayer et al. 2002; Pillsbury et al. 2002). Zebra mussel invasions can change

benthic invertebrate taxonomic composition and abundance by diverting pelagic primary

production to benthic food webs (Stewart and Haynes 1994; Rutherford et al. 1999; Ward

and Ricciardi 2007). Grazing by zebra mussels can limit food availability for pelagic

consumers (i.e., zooplankton, fish). This decreased primary production can have indirect

effects, including walleye (Sander vitreus) population declines (Koops et al. 2006) and

increased yellow perch growth (Mayer et al. 2000). Studies of zebra mussel food web

impacts have focused on large lakes (e.g., Great Lakes, Oneida Lake); however, smaller

inland lakes are also susceptible to invasion via movement of zebra mussels on boats, trailers and in bilge water from infested lakes (Leung et al. 2006). Smaller lakes (< 20 km2)

represent ~45% of total lake surface area in the United States and Canada (Hansen et al.

2010) and thus a large percentage of lakes susceptible to zebra mussel invasion have received

little attention.

Lake food web structure changes are likely with future zebra mussel invasions.

Quantifying food web structure and trophic flows is difficult due to the extensive quantitative

information required to describe complex aquatic food webs. Post invasion changes in food

web structure and trophic flows can be inferred from stable isotopes, but this approach does

not provide a holistic integrative view of lake ecosystems (Caitriona and Grey 2006; Nilsson

et al. 2011). Holistic quantitative descriptions of food webs in smaller inland freshwater

systems are few, but are needed to fully understand consequences of species invasions.

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Mass-balance food web models have been successfully used to quantify trophic flows

in food webs (Steele 2009; van Oevelen et al. 2010). ECOPATH is the most common mass-

balance model used to represent food webs and quantify trophic interactions among

ecosystem components (Christensen and Walters 2004). Since its introduction by Polovina

(1984), ECOPATH has been extended by Christensen and Pauly (1992) and become the

preeminent tool for modeling food webs—currently used by more than 5000 users in 164

countries (www.ecopath.org, accessed 01/01/2012). While ECOPATH has primarily been

used to understand trophic interactions in marine and estuarine systems, it has also been

successfully applied to freshwater systems (e.g., Fayram et al. 2006; Koops et al. 2006; Pine

et al. 2007).

Many smaller inland lakes in the Midwestern USA have common carp and are at-risk

to zebra mussel invasion. Whereas impacts of common carp in small Midwestern lakes have

been well described (e.g., Weber and Brown 2009; Jackson et al. 2010), impacts of invading

zebra mussels on phytoplankton production, food web components, and recreational fisheries

in these carp infested systems are largely unknown. To evaluate these effects, we

constructed four annual ECOPATH models to represent a eutrophic shallow lake food web

during the early stages of a zebra mussel invasion. The specific objectives of this paper were

to: i) construct annual ECOPATH models for a carp infested shallow eutrophic lake undergoing zebra mussel invasion, ii) evaluate total system consumption among food web groups and quantify phytoplankton and zooplankton consumption by food web components, iii) evaluate impacts and structuring effects of common carp and zebra mussels on biotic food web components and recreational fisheries.

METHODS

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Study area

Clear Lake is a 1474 ha shallow lake (mean depth = 2.9 m) located in the Western

Cornbelt Plains ecoregion of north central Iowa (43°08’N, 93°22’W; Figure 1). Annual economic value of the lake for vacation and recreational use is approximately 43 million dollars (CARD 2008). An open water and ice fishery, primarily for walleye, yellow bass

(Morone mississippiensis), and black bullhead (Ameiurus melas) is valued between 1-2.5 million dollars annually (S. Grummer, Iowa Department of Natural Resources, personal communication).

Clear Lake water quality has declined over the past century. Clear Lake has transitioned from a historically clear water state to a eutrophic/hypereutrophic turbid-water state characterized by frequent cyanobacterial blooms and simplified fish and plant communities (Niemeier and Hubert 1986; Carlander et al. 2001; Downing et al. 2001a; Wahl

2001; Egertson et al. 2004). A commercial fishery is used to reduce common carp biomass, with harvest exceeding 1400 metric tons since 1929. Watershed restoration is ongoing to reduce external nutrient loading. Zebra mussels were first detected on the eastern shore of

Clear Lake in 2005 (Figure 1), and biomass has increased dramatically over the 2007-2010 study period (Figure 2). Presently, zebra mussels occupy all firm substrate (e.g., gravel, rock, macrophytes) (Colvin et al. 2010).

Annual ECOPATH models

Annual ECOPATH models were used to model food web trophic flows over the 2007 to 2010 study period using a combination of data collected in Clear Lake and borrowed from similar lakes, and empirical relationships. ECOPATH is a mass-balance model that constrains production and consumption of food web groups by two master equations.

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Equation 1 partitions annual group production among predation, migration, fishery harvest,

and other mortality. Equation 2 partitions annual consumption among production,

respiration, and feces.

Production predation net migration biomass accumulation harvest

other mortality (1)

Consumption production respiration unassimilated food (2)

Equations 1 is specified in terms of rates for each food web group as equation 3,

Bi•PBi=∑ Bj•QBj•DCij+NMi•Bi+BAi•Bi+Fi•Bi+Bi•PBi•(1−EEi) (3)

where Bi is the biomass of group i, PBi is production to biomass ratio of group i, Yi the annual

harvest of group i, Bj is the biomass of predator j, QBi is the consumption to biomass ratio of

consumer i, DCij is the diet fraction of prey i for predator j, NMi is the annual net migration

(i.e., immigration–emigration) rate of group i, BAi the biomass accumulation rate for group i,

Fi is the annual fishing mortality rate of group i, and EEi is the ecotrophic efficiency for

group i. Equation 3 is subject to the constraint imposed by Equation 2, that group-specific

consumption must equal the sum of group-specific production, respiration, and unassimilated

food. ECOPATH requires B, PB, QB, Y, and DC values for each group in the model to

satisfy master equations 1 and 2 (Christensen and Pauly 1992). EEi is difficult to measure in practice and is estimated by solving the linear equation by generalized linear inverse given

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previous inputs. EE is constrained to range from 0 to 1. Groups with EE greater than 1 are

not balanced (i.e., biomass flows to sinks exceeds production). The food web model was

constructed by linking groups through group specific consumption of prey items in equation

3. Prey items and diet fractions required to assemble these trophic linkages were determined from a combination of existing lake-specific data and data from similar systems. All

ECOPATH models were constructed in ECOPATH with ECOSIM version 6.2.0.620

(Christensen and Walters 2004).

Data pedigree and model quality

Individual parameters used to balance each annual ECOPATH model were assigned a

data pedigree ranging from 0 (poor) to 1 (excellent) (Christensen et al. 2005). Pedigree index

(PI) values, calculated as the average pedigree of individual parameters, represent overall

confidence in ECOPATH model input parameters, where lower values have greater uncertainty. Data pedigree values can be used to provide reasonable bounds to individual

estimates (e.g., 0.8 = ±20%, 1 = ±10%). Model fit (t*) was calculated as PI•(N-2)0.5/(1-

PI2)0.5, where PI is the pedigree index and N is the number of groups in the model

(Christensen et al. 2005). This value assesses how locally rooted an ECOPATH model is, with high t* values reflecting a model based primarily on local information and low t* values reflecting a model based primarily on parameter values derived from similar systems.

ECOPATH allows data pedigrees to be assigned to PB, B, QB, Y, and DC values, therefore

PI values associated with these parameters were used to calculate t*. Since the same data sources were used to construct all four annual ECOPATH models, PI and t* were the same

for each year.

ECOPATH functional groupings

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Consumers.—Species represented in ECOPATH models were aggregated into 32

groups based on similar taxonomy and diet. Fish were represented by 21 groups. Yellow

bass, bluegill (Lepomis macrochirus), black bullhead, and walleye were numerically dominant (Colvin et al. 2010) and therefore two stanzas (i.e., age groupings) were used to capture important diet shifts between age 0 (<12 months) and age 1+ (>12 months) fish.

Pelagic zooplankton was represented by three groups: cladocerans, copepods, and rotifers.

Benthic macroinvertebrates were grouped as chironomids, non-chironomid benthic insects, zebra mussels, other bivalves (i.e., sphaeriidae), snails, benthic crustaceans (i.e., harpacticoid copepod), and worms (i.e., annelids, turbellarians, nematodes). Further information regarding consumer groupings can be found in Appendix 2.

Producers and detritus.—Producers were organized into five groups and detritus was

represented by a single group. Planktonic and benthic algae were aggregated into 4 groups

representing edible (e.g., Chlorophyta, Bacilloraphyta) and inedible (e.g., Cyanobacteria)

groups. An aquatic macrophyte group included submergent, emergent and floating

vegetation. A detritus group was used to accumulate biomass flows from unassimilated food

and senescent biomass. Further information regarding producers and detritus grouping can

be found in Appendix 2.

ECOPATH inputs

Biomass.—Group biomass (Bi) was estimated using lake- and year-specific

information derived from extensive field sampling. A full description of sampling designs

and estimation of biomass for each group are beyond the scope of this paper, but detailed

methodologies can be found in Appendix 1 and Colvin et al. (2010). Macrophyte coverage is

monitored by Iowa Department of Natural Resources (IADNR) and macrophyte biomass was

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estimated using a predictive relationship developed by Håkanson and Boulion (2002) relating macrophyte coverage to biomass. Biomass values were all assigned a data pedigree of 1 for all years, except macrophytes which were assigned a value of 0.4. Further information regarding biomass of groups used as basic inputs for annual ECOPATH models can be found in Appendix 2.

Production.—Annual production was estimated for fish and invertebrate groups using published production estimators. Fish production was estimated as

log10(P)=log10 (0.32)+0.94•log10(B)-0.17•log10Wmax, (4)

where P is production in (kg/ha/yr), Wmax is the maximum individual weight (g) observed and

B is biomass in kg/ha (Downing and Plante 1993). Annual benthic invertebrate production was estimated using Kalff’s (2003) modification to Plante and Downing’s (1989) estimator

log10(P)=log10(0.073)+0.73•log10(B). (5)

PB was calculated by dividing annual production by biomass. PB values for primary producers were acquired from Jorgenson (1979). Consumer PB values were assigned a data pedigree value of 0.5 and primary producers were assigned a value of 0.1. Further information regarding PB values of groups used as basic inputs for annual ECOPATH models can be found in Appendix 2.

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Biomass accumulation.—A common assumption of ECOPATH models is that food

web components are in steady state (i.e., BA=0). BA for system groups was calculated as the annual biomass minus the previous year’s estimate (Christensen et al. 2005). BA was

assumed to be 0 for the 2010 ECOPATH model, since comparable data for 2011 was

unavailable. See Appendix 2 for group-specific BA values.

Consumption.—Consumption to biomass ratios (QB) were estimated from published

predictive relationships and assumed gross growth efficiencies (GGE). Annual consumption

(Q) by fish groups was estimated using the empirical consumption estimator for freshwater

fish developed by Liao et al. (2005). Fish group QB was estimated by dividing the group-

specific estimates of Q by B except for age 0 fish. Age 0 fish were assumed to have a GGE

of 0.6 to reflect that younger fish have a higher PQ (Christensen et al. 2005). Invertebrate

QB was estimated by dividing PB by PQ (i.e., gross growth efficiency). PQ was assumed to be 0.3 for all invertebrate groups except bivalves exclusive of zebra mussels (PQ=0.26), copepods (PQ=0.35), cladocerans (PQ=0.27) and rotifers (PQ=0.24) (Straile 1997).

Invertebrate QB estimated from assumed PQ were compared to QB for similar systems to ensure reasonable estimates were used (e.g., Oneida Lake, Jaeger 2006). A data pedigree value of 0.5 and 0.2 was assigned to each QB and PQ value. Further information regarding

QB values of groups used as basic inputs for annual ECOPATH models can be found in

Appendix 2.

Diet composition.—Diet composition (DC) for consumer groups was estimated using lake-specific diet information when available and published diet compositions for groups lacking local information. Fish diet compositions were compiled from a combination of existing studies in Clear Lake, similar nearby lakes, and from published records (e.g.,

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Effendie 1968; Carlander 1969; Liao et al. 2002). Diet compositions for benthic invertebrates and zooplankton were compiled from published sources (e.g., Thayer et al.

1997; Thorp and Covich 2001; Voshell 2002). Data pedigree values of 0.2 to 0.7 were assigned to each DC to reflect the quality of the data used in estimates. See Appendix 2

Table 3 for diet item sources and Appendix 2 for year-specific diet compositions used in annual ECOPATH models.

Harvest.—Annual commercial and recreational fishery harvest values were available for Clear Lake and used in the ECOPATH model. Commercial fishery harvest of common carp and bigmouth buffalo (Ictiobus cyprinellus) biomass are reported directly to IADNR by commercial fisherman. An expandable creel survey was used to estimate the annual harvest associated with the recreational open water and ice fishery (McWilliams 1984; Colvin et al.

2010). Commercial and recreational harvest was summed within years for ECOPATH inputs

(i.e., Yi). See Appendix 2 for specific harvest amounts used in ECOPATH models. A data pedigree value of 1 was assigned to all harvest values.

Stocking.—Sportfish species are stocked annually into Clear Lake, including walleye, channel catfish (Ictalurus punctatus), and esocids (e.g., muskellunge Esox masquinongy, northern pike Esox lucius). Annual species-specific stocking biomass was calculated by multiplying the number of fish stocked by the mean weight provided from hatchery records.

When mean weight was unavailable, values for similar sized fish in Carlander (1969) were used. See Appendix 2 for annual stocking values.

Import.—Data from Clear Lake were used to estimate import of phytoplankton and zooplankton. Phytoplankton and zooplankton flow into the lake from Ventura Marsh and were quantified every 2 weeks during the ice free season in 2008-2010 (Figure 1). Import to

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the system was estimated as the mean biomass (mg wet weight/L) multiplied by the annual

inflow to Clear Lake (IADNR TMDL & Water Quality Assessment Section 2005). Similar

data were not available for 2007, so import was assumed to be the average of 2008-2010.

See Appendix 2 for annual import values.

Export.—Biomass exported from the Clear Lake was estimated for phytoplankton and

zooplankton groups based on lake flushing rate. Planktonic phytoplankton and zooplankton

loss rates were estimated as the inverse of lake retention time (1.9 years) (Scheffer 1998;

IADNR TMDL & Water Quality Assessment Section 2005). See Appendix 2 for annual

export values.

ECOPATH mass-balance

Annual ECOPATH models were mass-balanced by manual iterative adjustment of

ECOPATH inputs for groups where EE values were not between 0 and 1. After initial

solving of the set of linear equations, groups with the highest ecotrophic efficiencies (EE)

were identified and basic inputs were adjusted until EE was between 0 and 1. The process

for adjusting problematic groups followed three steps: 1) adjust diet compositions of

predators exerting overly high predation, 2) adjust PB and QB values, and 3) adjust B.

Attempts were made to keep adjustments within the a priori specified confidence range

based on the data pedigree (Christensen et al. 2005). Once EEs for all groups were between

0 and 1, the ECOPATH model was judged to be balanced and used to quantify trophic flows

and network indices.

System and group consumption

Consumption of biomass is a significant component of ecosystem functioning (i.e., cycling of matter). Annual system consumption (Q) was calculated from the mass-balanced

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ECOPATH models for consumers as the sum of all consumption within the system. Total

consumption by common carp, zebra mussels, and all other consumers was calculated and

related to total system consumption. The proportion of system consumption occurring due to

common carp and zebra mussels was calculated as group-specific consumption divided by

total system production. Dominant system consumers were identified by ranking proportion

of total system production. Annual consumption of top consumers, common carp, and zebra

mussels were compared within each study year.

Mixed trophic impacts

Impacts of common carp and zebra mussels on other groups in the food web and the

recreational fishery were evaluated using a mixed trophic impact analysis. Mixed trophic

impacts (MTI) quantifies the direct and indirect impacts of groups on each other, with values

scaled to range from -1 (large negative impact) to 1 (large positive impact). Calculation of

net impacts are the basis for MTI values and are calculated as the difference of the fraction of

the prey i in the diet of the predator j (DCji) (i.e., positive impacts), and the fraction of total

consumption of i used by predator j (i.e., negative impacts) (Ulanowicz and Puccia 1990).

MTI values for each group i on group j were calculated as the product of all possible net

impacts of group i and group j. Negative MTI values reflect a net negative impact (i.e.,

direct predation, competition) and positive values reflect a net positive effect (e.g.,

facilitation, increased prey) (Christensen and Walters 2004; Christensen et al. 2005; Janjua

and Gerdeaux 2009). MTI of group i on group j () was calculate using the network

analysis plugin of ECOPATH (Ulanowicz and Puccia 1990; Christensen et al. 2005). Non-

native species’ impact on food web groups and the recreational fishery was assessed by examining MTI values for common carp and zebra mussels. Phytoplankton and zooplankton

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are important biotic components of water quality in shallow lentic systems. Long-term lake

restoration may be limited if predation on zooplankton limits their ability to control of

phytoplankton production (Sondergaard et al. 2007). MTI values for zooplankton and edible phytoplankton groups were examined to identify groups exerting negative impacts. Mixed trophic impacts of non-native species, zooplankton, and phytoplankton were graphically assessed. It should be noted that this analysis does not include abiotic and biotic system feedbacks, so results should be interpreted cautiously.

Keystoneness

Structuring of the food web by system groups was assessed using keystone analysis.

Keystone analysis calculates an index of keystoneness that quantifies the relative impact of a group on the entire system, accounting for biomass (Libralato et al. 2006; Coll et al. 2009).

In other words, groups that have a strong structuring role in the food web but have relatively small biomass are keystone groups (Power et al. 1996; Coll et al. 2009). Keystoneness is calculated from the contribution of component biomass to total system biomass ()

, (6) ∑

where is the biomass of group i and the sum of Bk is the total system biomass.

Total system impact is calculated as

∑ , (7)

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where is the MTI of group i on group j.

An index of keystoneness (KS) is calculate for each group as

• •1, (8)

where and were previously defined. When is plotted against , groups with large

and values provide strong structuring effects on the system (Power et al. 1996;

Libralato et al. 2006). See Libralato et al. (2006) for a complete derivation of this index.

Keystoneness and overall impact values were calculated using the network analysis plugin of

ECOPATH. Changes in keystoneness for common carp, zebra mussels, and the remaining food web groups over the study period were graphically assessed by plotting against .

Changes in position and ranking among groups indicate changes in a specific group’s

keystoneness. For example, one may expect the structuring role of zebra mussels in a food

web would increase over time during an invasion. However, if this structuring effect is not

biomass dependent the group’s keystoneness index value would increases over the study

period.

RESULTS

ECOPATH models.—A total of 38 groups were used to represent consumers,

producers, and detritus in the Clear Lake ecosystem (Table 1). Data pedigrees varied from

0.1 to 1 for B, PB, QB, Y, and DC. Annual ECOPATH model PI was 0.6 and t* was 4.4.

Trophic levels varied from 1 to 4.1. Over the study period common carp and zebra mussel

biomass increased. The lowest ecotrophic efficiencies (fraction of production used by the

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food web) were lowest for phytoplankton groups, as would be expected for a eutrophic system.

Consumption.—Total system consumption was dominated by consumers utilizing detritus and benthic production. Total system consumption was 228, 264, 263, and 228 g/m2 in 2007 to 2010 respectively. Chironomids, common carp, and zebra mussels were dominant system consumers among years (Figure 3). Annual consumption by chironomids varied from

35 to 114 g/m2, which was 17 to 42% of total system consumption, and annual common carp

consumption varied from 42.1 to 58.9 g/m2, representing 16 to 26% of total system

consumption. Annual zebra mussel consumption varied from 24 to 120 g/m2, which was 9 to

46% of total system consumption. Total system consumption by zebra mussels increased

from 9% in 2007 to 40% in 2010. Proportion of total system consumption by consumers

exclusive of chironomids, common carp and zebra mussels was an order of magnitude lower

than the three dominant system consumers.

Mixed trophic impacts

Common carp.—Common carp trophic impacts were greatest on groups they compete with, groups they consume, and the commercial fishery (Figure 4). As expected, common carp positively impacted the commercial fishery. Common carp impacts were negative for prey groups, and for bigmouth buffalo, which was an indirect impact of commercial fishery by-catch.

Zebra mussels.—Zebra mussel trophic impacts increased over the study period with increasing zebra mussel biomass (Figure 4). Negative trophic impacts of zebra mussels were observed for groups competing with zebra mussels for pelagic phytoplankton. Zebra mussels

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also exhibited indirect negative impacts on age 0 yellow bass and lower trophic level fish

groups via competition with zooplankton for edible planktonic algae.

Edible phytoplankton and zooplankton.—Food web groups impacting edible

phytoplankton and zooplankton groups were consistent among years (Figure 5). Zebra mussels had the largest negative impact on edible planktonic phytoplankton. Relative to zebra mussels, zooplankton groups had a negligible impact on edible planktonic phytoplankton. Predation by age 0 yellow bass and bigmouth buffalo exhibited negative impacts on both copepod and cladoceran groups. However negative impacts decreased over the study period for these two groups. Zebra mussels had an increasingly negative impact on both zooplankton groups. Phytoplankton was the only group that exhibited a consistent positive impact of sizable magnitude on zooplankton groups.

Recreational fishery.—Groups having an impact on the recreational fishery were similar over the study period (Figure 5). Sport fish (i.e., walleye, yellow bass) exhibited positive impacts on the recreational fishery as dominant components of the fishery.

Chironomids had a positive effect on the recreational fishery among all years, highlighting the importance of this group as sport fish prey. Negative impacts from any group were negligible.

Keystoneness

Keystoneness indices varied over the study period for zebra mussels and common carp. Zebra mussel keystoneness increased from 14th to 6th over the study period indicating

increased food web structuring by zebra mussels (Figure 6). Common carp keystoneness

remained relatively constant (13th to 17th) despite increasing biomass over the study period indicating that common carp are not having a relatively strong effect on structuring trophic

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flows, but this result should be interpreted cautiously since ECOPATH does not include indirect effects of common carp bioturbation on trophic flows. Chironomids and benthic algae were ranked within the top five keystone groups across study years. Overall, the groups with the largest keystoneness and relative impact were similar among years but rank was variable (Figure 6).

DISCUSSION

ECOPATH models facilitated understanding of non-native species impacts in Clear

Lake, a complex aquatic system. Overall model quality (PI) was 0.59, which was high relative to models published in other studies (PI values ranging from 0.45 to 0.53) (Pinnegar and Polunin 2004; Fayram et al. 2006). This study represents one of the few attempts to understand freshwater aquatic food webs undergoing a zebra mussel invasion. Relative to lakes in which previous food-web based studies of zebra mussel impacts have been conducted (e.g., Yu and Culver 1999; Yu and Culver 2000; Jaeger 2006; Miehls et al. 2009),

Clear Lake is small, shallow, eutrophic, and is representative of systems predicted to be at high-risk to zebra mussel invasion (Drake and Bossenbroek 2004; Whittier et al. 2007). To our knowledge this is the first holistic food web characterization of a system undergoing zebra mussel invasion, since previous published works focused on pre- and post-invasion food web dynamics.

Common carp

Common carp ranked second or third in total system consumption in all four study years. It is noteworthy that such dominance of total system consumption can occur, but with relatively less overall system impact as measured by MTI and keystoneness. This is likely due to common carp primarily exploiting benthic production. Excess phytoplankton settling

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provides a continuous energy input to the benthic portion of the food web, supporting high levels of benthic secondary production. Eutrophic systems like Clear Lake provide ample detritus and chironomid production which are used by benthic omnivores like common carp.

Limited food web impact by common carp may at first seem counterintuitive, since numerous studies have documented deleterious effects of common carp in aquatic systems.

However, quantification of common carp food web impacts have been limited to experimental mesocosms (e.g., Parkos et al. 2003), ponds (e.g., Chumchal et al. 2005), exclosures within aquatic systems (e.g., Lougheed et al. 1998), or following large-scale biomass reductions (e.g., Schrage and Downing 2004). While these previous studies focused on evaluating impacts using system-level metrics (e.g., species abundance) to determine common carp effects, studies evaluating trophic interactions of common carp within whole- lake food webs are few.

High rates of consumption by benthivorous fishes can have consequences for nutrient cycling in lake ecosystems (Sereda et al. 2008). This is especially true for common carp, where high biomass and a diet dominated by benthic production can alter lake nutrient levels via excretion (Lamarra 1975; Schrage and Downing 2004). In particular, benthivorous fish excrete P at higher rates than piscivorous fishes due to differences in elemental stoichiometry

(Jobling 1994; Sereda and Hudson 2010). Common carp excretion is a function of consumption, which in turn is a function of biomass (Jobling 1994; Liao et al. 2005; Sereda and Hudson 2010). However, ECOPATH food web analyses do not address nutrient cycling, which can have significant bottom up effects on phytoplankton production in freshwater systems (Schaus et al. 1997; Schrage and Downing 2004; Conroy et al. 2005). Critical

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examination of nutrient flows to evaluate potential bottom up effects will be needed to fully

evaluate common carp effects in lake ecosystems.

Zebra mussels

Interactions among zebra mussels and phytoplankton are likely mediated by primary

production which is primarily a function of available nutrients. Eutrophication has created

favorable trophic conditions for invading zebra mussels in Clear Lake. Decades of increased

external nutrient loading have resulted in excess phytoplankton production that can be grazed

by filter-feeding zebra mussels. However, the detrital “rain” of settling excess phytoplankton

production can cover hard substrate, in turn limiting suitable zebra mussel habitat in this

system (Coakley et al. 2002). Future zebra mussels impacts in Clear Lake will depend on

their capacity to expand beyond the limited hard substrate (<2% of lake area) that is preferred

habitat (Coakley et al. 2002). However, zebra mussels have been shown to expand their

habitat by attaching to shells of both living and dead zebra mussels (Mortl and Rothhaupt

2003). Zebra mussel reefs can grow by gregarious recruitment until large aggregations break off creating a slow outward expansion from areas with hard substrates to normally unsuitable soft substrates (Coakley et al. 1997). Zebra mussels can also conglomerate soft sediment using byssal strands to facilitate soft sediment colonization (Berkman et al. 1998). However, these expansion processes are slow relative to the initial zebra mussel invasion and their consequences for Clear Lake are uncertain.

As phytoplankton filter feeders, zebra mussels divert energy from pelagic to benthic portion of the food web, and potentially reduce trophic support for pelagic consumers and ultimately recreational fisheries. Zebra mussels occupy a similar trophic niche as zooplankton in lake ecosystems. Both groups can improve water quality by reducing

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phytoplankton abundance through grazing. However, in other respects zebra mussels and

zooplankton function differently within the system. Zooplankton move energy up the pelagic

food web, providing support for higher trophic levels, whereas zebra mussels shunt energy

away from pelagic food webs by converting phytoplankton into pseudofeces (Berg et al.

1996; Vanderploeg et al. 2001) and zebra mussel biomass which is primarily utilized by

benthic consumers (e.g., amphipods, insects, benthivorous fishes) (Stewart and Haynes 1994;

Stewart et al. 1998; Magoulick and Lewis 2002). Shunting energy away from the pelagic food web can alter abundance and growth of sport fishes (Thayer et al. 1997; Rutherford et

al. 1999; Mayer et al. 2000; Miehls et al. 2009). However, some fish species can prey

directly on zebra mussels and invertebrates associated with zebra mussel colonies, potentially

enhancing those populations and fisheries they support (Magoulick and Lewis 2002; Watzin

et al. 2008).

Keystoneness

A keystone species is a group with a disproportionately large food web effect relative

to its biomass (Paine 1969; Paine 1995). In shallow lake ecosystems in a clear water state, it

is likely that small biomasses of apex predators (e.g., esocids) should structure shallow lake

food webs (Scheffer 1998). However it may be that keystone group dynamics are variable,

especially as an ecosystem transitions between alternative stable states, such as the turbid and

clear water states experienced by shallow lakes (Scheffer 1998). In a turbid water state

keystone groups are likely adapted to foraging in turbid conditions, while in a clear water

state, keystone species are likely visual, ambush predators. Rankings of keystone groups

varied among walleye, yellow bass, flathead catfish, and esocids representing groups adapted

to turbid and clear waters. Variability in keystoneness rankings among turbid and clear water

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fish taxa indicate that the lake may be in a transitional period between a turbid and a clear

water state.

Common carp and zebra mussel keystoneness were intermediate relative to other

biotic groups in Clear Lake (i.e., walleye, yellow bass, chironomids). This result is

counterintuitive, since previous research has demonstrated that common carp and zebra

mussels can have highly significant ecosystem effects. Intermediate levels of keystoneness

suggest that common carp and zebra mussel effects on ecosystem structure may not be limited to just trophic interactions. For example, common carp alter phytoplankton and macrophyte abundance indirectly via physical resuspension (i.e., bioturbation) of benthic sediments during foraging and spawning and altered nutrient cycling (Breukelaar et al. 1994;

Cline et al. 1994; Schrage and Downing 2004; Rahman et al. 2008). Similarly, zebra mussel excretion can alter lake nutrient levels, which can indirectly alter the phytoplankton

assemblage (Arnott and Vanni 1996; Conroy et al. 2005; Higgins et al. 2008). Both common

carp and zebra mussel excretion can have bottom up effects on ecosystem structure (Arnott

and Vanni 1996; James et al. 2000; Schrage and Downing 2004; Qualls et al. 2007; Higgins

et al. 2008). Indirect effects of common carp and zebra mussels via nutrient cycling and

changes in environmental characteristics (e.g., turbidity) were not captured in our ECOPATH

food web analysis and therefore not represented in the keystoneness analysis. In addition to

strict food web effects, common carp and zebra mussel are likely acting as ecosystem

engineers within the system.

Recreational fishery

Sport fish groups had the largest impact on recreational fisheries. Walleye and

yellow bass dominate recreational yield, which in turn are supported primarily by benthic

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invertebrates (i.e., chironomid) and age 0 yellow bass (Colvin et al. 2010). However, natural

production and recruitment of age 0 walleye is believed to be limited in Clear Lake (S.

Grummer, IADNR personal communication). This is likely due to insufficient or temporally mismatched zooplankton availability (Mayer and Wahl 1997; Peterson et al. 2006). Walleye

at a piscivorous size (i.e., fingerling) are stocked to supplement natural production, bypassing this apparent pelagic zooplankton bottleneck. This bypass minimizes direct walleye dependence on pelagic zooplankton production, but not on small-bodied pelagic forage fish, such as age 0 yellow bass that dominate walleye diet (Spykerman 1973; Inmon 1974;

Bulkley et al. 1976). Age 0 yellow bass prey heavily on zooplankton and only casually on other prey items (i.e., chironomids), while age 1+ Yellow bass prey heavily on chironomids in Clear Lake (Kraus 1963). Few food web groups impacted the recreational fishery, suggesting that that the fishery may be part of a subsystem with limited connectedness to the food web because super-abundant chironomid and age 0 yellow bass production provide the majority of trophic support for the recreational fishery dominated by age 1+ yellow bass and walleye.

Connectedness of recreational fisheries within freshwater food webs is a topic requiring further research. A diverse recreational fishery can have a portfolio effect where low yields in one sport fish population are compensated by high yields in another population, thereby buffering changes in overall recreational fishing yield due to variability in sport fish abundance (Hilborn et al. 2003; Schindler et al. 2010). Resilience of the recreational fishery to system changes may be a function of the connectedness within the lake food web. In other words, changes in chironomid and age 0 yellow bass production may have significant effects on yellow bass and walleye recreational fishery yield, but limited impact on the abundance of

126

other sport fishes. Additionally, lake restoration may reduce chironomid production

(Langdon et al. 2006; Luoto 2011), which will likely have consequences for future recreational fishery yield of fishes dependent on this resource.

Food web and lake restoration

Predation on zooplankton potentially limits long term success of lake restoration

(Sondergaard et al. 2007). MTI analysis indicated that zooplankton were negatively

impacted by age 0 yellow bass. Zooplankton consumption by age 0 yellow bass was 12 to

47% of annual zooplankton production, likely limiting zooplankton abundance. This is

similar to other shallow lake systems where zooplanktivorous fish (e.g., age 0 yellow perch)

suppress zooplankton, thereby limiting lake restoration success, (Perrow et al. 1995; Perrow

et al. 1996). In Clear Lake, zebra mussels may increase the likelihood of successful lake

restoration by compensating for reduced zooplankton abundance due to age 0 yellow bass

predation. Despite the fact that long-term lake restoration success may be limited by age 0

yellow bass, managing this abundant and popular sport fish to minimize negative

zooplankton impacts will pose a significant ecological and social challenge.

The majority of past research in lake ecosystems has focused primarily on pelagic

food web groups. The disparity of pelagic versus benthic research was so notable that

Vadeboncoeur et al. (2002) found that the number of published papers referencing benthic or

benthic and pelagic systems were at most 20% of those referencing pelagic systems only.

Whole-lake ecosystem function and dynamics are comprised of both benthic and pelagic

pathways. Within lake ecosystems, consumption is a critical ecosystem function to cycle

biomass and nutrients. Consumption occurring through benthic pathways in Clear Lake

dominated system consumption, supporting the argument that benthic food webs are

127

significant in whole-lake ecosystems and should be considered if ecosystem level understanding is desired (Vadeboncoeur et al. 2002).

Holistic food web models like ECOPATH provide unique and detailed insight to trophic interactions. However, environmental feedbacks are not represented in ECOPATH models. Biotic components of shallow lake ecosystems influence primary production through bottom up and top down processes contributing to environmental feedbacks (Schaus et al. 1997; Vanni 2002; Schrage and Downing 2004). Common carp and invading zebra mussels contribute to nutrient cycles, affecting primary production (Arnott and Vanni 1996;

Downing et al. 2001b; Schrage and Downing 2004). Assessing non-native species impacts within lake food webs undergoing restoration will require a model that couples food web, nutrient and water quality dynamics, which is the topic of Chapter 5.

ACKNOWLEDGMENTS

The data for this research was the result of numerous members of the Iowa

Department of Natural Resources personnel that we are grateful to, including: J. Wahl, S.

Gummer, J. Christenson, E. Thelen, D. Fjelt, J. Larschied, K. Bogenshutz, J Euchner. Lisa

Fascher provided assistance in field work and water quality data that we would like to acknowledge. We are also grateful to Iowa State University personal for assisting in field and laboratory work including V. Hengtes, Z. Jackson, J. Koch, T. Neebling, J. Lore, J.

Schmitz, C. Penne, D. Wooley, J. Norlin, G. Scholten, E. Kiefer, M. Sundberg, J. Skiff, and

K. Rebarchek. We are also grateful for project support from David Knoll. This project was supported in part by the Department of Natural Resource Ecology and Management at Iowa

State University, Iowa Cooperative Fish and Wildlife Research Unit, and the Iowa

128

Department of Natural Resources. The use of trade names or products does not constitute

endorsement by the U.S. Government.

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Ventura 4777000 Clear Lake Marsh

4776000

4775000 Northing

4774000

4773000 Sand (4%) Rock and Gravel (2%) Silt (92%) Emergent Vegetation (2%) N

460000 462000 464000 466000 468000

Easting

Figure 1. Substrate and vegetated areas of Clear Lake, north central Iowa (inset). Lake inflow travels east from Ventura Marsh. Percentages correspond to lake area of each substrate classification. The circle located on the eastern shore represents the approximate location of the first detection of zebra mussels in the system.

144

10.00 ) 2 1.00 Dry weight (g/m weight Dry

0.10

2007 2008 2009 2010

Year Figure 2. Zebra mussel biomass dynamics over the study period. Dots represent mean lake biomass estimates (dry weight; g/m2) and lines denote bootstrap 95% confidence intervals. Note that data is plotted on a log10 scale on the y-axis. See Appendix 1 for methodologies used to estimate zebra mussel biomass.

145

2007 2008 2009 2010

Chironomidae

Common c ar p

Zebra mussels

Cladoceran

Worms

Benthic crustaceans (non amphipod)

Copepod

Bigmouth buffalo

Yellow bass (1+)

Rotif er

Yellow bass (0)

Walleye (1+)

Benthic insects

Black bullhead (1+)

Other bivalves

Snails

Other benthivores

Es oc ids

Benthic crustaceans (Amphipods)

Food web group Channel catfish

Flathead catfish

Walleye (0)

White bass

Crappie

Bluegill (1+)

Yellow perch

Minnow s and shiners

Black bullhead (0)

Black bass

Darters

Sunfish

Bluegill (0)

0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 Proportion of total system consumption

Figure 3. Proportion of total system consumption for food web groups. Groups are ranked on the y-axis by decreasing proportion for the 2007 year.

146

Impacted group Bent B enthh ic crustaceans (n ic cr ustace M P R B lan Commercial fishery Blac in Be e lack bullhea Oth B no Y an k P crea C Fla ig Yellowel bass (0) nth to l Common carp e w s BenthicZebra i Ot mussels n an k han m s Y lo C (A o i B ic M ti b Bl r b thea Walleye (1+)outh bu w h n h c e kt on Black bassu Bluegill (0) WallWh and s el ironomidmphipo amphipod) er C b nthic b o a llheadu (0) n en lo b Coplad l lue nicg crAl a eg el catfis Crad w a b ue l F thivor D Eso e ite Su ss W iv o ophDe d ill ca a y h p n S ceraRoti gre Algae is ( rte e ba f in n e or s nail a e r t h 1 (1+) p tfis c ( fa f r (1+ ds) e lvespod eeng ytesritus ery +) e pi ids 0 s e ish ch m a cts f e ae h s e h rs ) s lo rs ) s e s n er n

Zebra Mussels: 2007

2008

2009

2010

Common carp: 2007

2008

Impacting group 2009

2010

Figure 4. Common carp and zebra mussels mixed trophic impacts on food web groups. Positive values represent a net positive impact, negative values denote a net negative impact, and values of 0 represent no impact. Trophic impact values are scaled to vary from -1 to 1 with increasing values (postive or negative) having stronger impacts. Circles are proportional to the size of the impact. Open circles represent a positive impact and closed circle represent negative impacts.

147

Impacted group Pl Pl Pl Pl R R Rec. Fishery:R 2009 Cladoceran:CladocCl 2007CladocBen. Ben. Ben. Ben. an an an an ec. ec ec. Co Cop Cop Cop ad . F k. k k. k F Fishe p oce . Al . Al epod:epod 2 ep epod: Algae:Algae 2 Algae:Algae 2 Algae Algae ishery:is 2007 e e g ga her od: ran ran: ran ae: 20 e: y ry: 2 : : : : : : 20 20 20 : 2008 200 : 2010 20 20 20 20 20 20 0 0 0 007 08 0 10 07 08 09 10 07 0 09 10 08 10 9 9 8

Recreational Fishery Commercial fishery Detritus Macrophytes Planktonic Algae Planktonic blue green Benthic Algae Benthic blue green Rotifer Cladoceran Copepod Other bivalves Zebra mussels Snails Benthic insects Benthic crustaceans (non amphipod) Benthic crustaceans (Amphipods) Chironomidae Worms Yellow bass (1+) ng group Yellow bass (0) ti Yellow perch Sunfish Minnows and shiners mpac

I Bigmouth buffalo White bass Walleye (0) Walleye (1+) Esocids Darters Flathead catfish Crappie Other benthivores Channel catfish Bluegill (0) Bluegill (1+) Black bullhead (0) Black bullhead (1+) Black bass Common carp

Figure 5. Food web groups’ mixed trophic impacts on the recreational fishery, edible algae, and zooplankton. Positive values represent a net positive impact, negative values denote a net negative impact, and values of 0 represent no impact. Trophic impact values are scaled to vary from -1 to 1 with increasing values (postive or negative) having stronger impacts. Circles are proportional to the size of the impact. Open circles represent a positive impact and closed circle represent negative impacts.

148

A: 2007 1 B: 2008 1 2 2 4 354 0.0 5 3 ZM ZM

-0.5

CC CC -1.0 1 Esocids 1 Flathead catfish 2 Flathead catfish 2 Walleye (1+) 3 Chironomidae 3 Chironomidae -1.5 4 Yellow bass (1+) 4 Benthic Algae 5 Benthic Algae 5 Yellow bass (1+) ZM Zebra mussels ZM Zebra mussels -2.0 CC Common carp CC Common carp

C: 2009 ZM 1 D: 2010 1 2 ZM 2 543 4 3 Keystoneness 0.0 5

-0.5 CC CC

-1.0 1 Walleye (1+) 1 Esocids 2 Esocids 2 Flathead catfish 3 Yellow bass (1+) 3 Benthic Algae -1.5 4 Benthic Algae 4 Chironomidae 5 Chironomidae 5 Zebra mussels ZM Zebra mussels ZM Zebra mussels -2.0 CC Common carp CC Common carp

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Relative total impact

Figure 6. Plots of relative total impact (x-axis) and keystoneness index (y-axis). Relative total impact quantifies the overall total impact of a food web group on the food web. Keystoneness indexes the impact of a food web group after accounting for its relative biomass. Groups with a high relative impact and keystoneness exert a strong structuring effect on the food web (Power et al. 1996; Libralato et al. 2006). Black dots denote common carp and zebra mussels. Numbers in the legend correspond to numbers for group in plots.

Table 1. Basic estimates for mass-balanced annual ECOPATH models. Group Trophic level B P/B Q/B EE P/Q 2007 Common carp 2.281 15.230 0.409 3.485 0.901 0.117 Black bass 3.699 0.010 0.920 3.567 0.235 0.258 Black bullhead (1+) 2.824 0.600 0.750 3.200 0.005 0.234 Black bullhead (0) 3.048 0.023 2.100 3.500 0.396 0.600 Bluegill (1+) 3.018 0.040 1.300 3.552 0.906 0.366 Bluegill (0) 3.035 0.005 2.271 3.785 0.324 0.600 Channel catfish 3.209 0.152 0.683 3.526 0.544 0.194 Other benthivores 2.634 0.296 0.661 3.517 0.003 0.188 Crappie 3.084 0.043 0.948 3.543 0.935 0.268 Flathead catfish 4.046 0.200 0.700 3.000 0.006 0.233 Darters 3.048 0.008 1.846 3.566 0.607 0.518

Esocids 4.059 0.216 0.800 3.500 0.009 0.229 149 Walleye (1+) 3.068 0.850 0.900 3.500 0.987 0.257 Walleye (0) 3.197 0.038 11.620 19.367 0.072 0.600 White bass 3.225 0.123 0.950 3.529 0.641 0.269 Bigmouth buffalo 3.056 1.600 0.454 3.496 0.378 0.130 Minnows and shiners 3.05 0.020 2.500 3.557 0.968 0.703 Sunfish 3.03 0.007 1.900 3.595 0.773 0.529 Yellow perch 2.964 0.022 1.300 3.552 0.940 0.366 Yellow bass (0) 3.027 0.900 2.200 3.667 0.111 0.600 Yellow bass (1+) 3.059 1.100 1.300 3.500 0.976 0.371 Worms 2.063 4.341 0.900 3.000 0.271 0.300 Chironomidae 2.054 21.633 1.800 6.000 0.811 0.300 Amphipods 2.000 0.020 8.600 28.667 0.904 0.300 Benthic crustaceans 2.000 2.528 1.422 4.741 0.590 0.300 Benthic insects 2.010 0.221 2.745 9.151 0.669 0.300 Snails 2.000 0.090 4.073 13.575 0.958 0.300 Zebra mussels 2.000 0.199 36.000 120.000 0.979 0.300 Other bivalves 2.000 0.103 3.376 12.966 0.402 0.260

Group Trophic level B P/B Q/B EE P/Q Copepod 2.116 0.610 4.400 12.571 0.995 0.350 Cladaceran 2 0.720 6.200 22.978 0.990 0.270 Rotifer 2 0.060 15.000 63.149 0.127 0.238 Benthic blue green 1 8.571 85.000 - 0.054 - Benthic Algae 1 14.430 113.000 - 0.057 - Planktonic blue green 1 108.516 86.000 - 0.001 - Planktonic Algae 1 1.834 113.000 - 0.162 - Macrophytes 1 0.259 8.000 - 0.152 - Detritus 1 14357.150 - - 0.004 - 2008 Common carp 2.213 13.800 0.500 3.484 0.842 0.144 Black bass 3.699 0.005 1.053 3.573 0.016 0.295 Black bullhead (1+) 2.820 0.600 1.250 3.000 0.017 0.417

Black bullhead (0) 3.048 0.068 2.300 3.833 0.216 0.600 150 Bluegill (1+) 3.018 0.040 1.500 3.100 0.909 0.484 Bluegill (0) 3.035 0.006 2.271 3.785 0.390 0.600 Channel catfish 3.200 0.070 0.900 3.537 0.922 0.254 Other benthivores 2.634 0.002 2.800 3.594 0.684 0.779 Crappie 3.084 0.015 2.300 3.560 0.846 0.646 Flathead catfish 4.042 1.250 0.436 3.000 0.012 0.145 Darters 3.048 0.006 2.450 3.584 0.571 0.684 Esocids 4.070 0.166 0.629 3.525 0.000 0.178 Walleye (1+) 3.080 0.900 1.100 3.100 0.856 0.355 Walleye (0) 3.197 0.085 4.587 7.645 0.230 0.600 White bass 3.225 0.336 0.600 3.516 0.685 0.171 Bigmouth buffalo 3.056 2.898 0.415 3.487 0.819 0.119 Minnows and shiners 3.050 0.010 3.000 4.000 0.566 0.750 Sunfish 3.030 0.002 2.600 3.624 0.722 0.717 Yellow perch 2.964 0.015 1.900 3.563 0.471 0.533 Yellow bass (0) 3.027 0.476 2.800 4.667 0.222 0.600 Yellow bass (1+) 3.059 1.750 1.800 3.100 0.975 0.581

Group Trophic level B P/B Q/B EE P/Q Worms 2.063 1.591 3.000 10.000 0.357 0.300 Chironomidae 2.054 8.400 3.960 13.200 0.908 0.300 Amphipods 2.000 0.080 3.875 12.918 0.636 0.300 Benthic crustaceans 2.000 1.136 3.000 10.000 0.541 0.300 Benthic insects 2.010 0.200 3.362 11.206 0.472 0.300 Snails 2.000 0.020 5.678 18.927 0.672 0.300 Zebra mussels 2.000 7.125 1.075 3.584 0.020 0.300 Other bivalves 2.000 0.044 4.236 16.271 0.090 0.260 Copepod 2.116 1.300 2.800 8.000 0.850 0.350 Cladoceran 2.000 1.300 3.400 12.601 0.963 0.270 Rotifer 2.000 0.604 3.200 13.472 0.126 0.238 Benthic blue green 1.000 1.000 98.000 - 0.354 - Benthic Algae 1.000 0.950 132.000 - 0.662 -

Planktonic blue green 1.000 174.208 85.000 - 0.002 - 151 Planktonic Algae 1.000 7.604 113.000 - 0.047 - Macrophytes 1.000 0.350 8.000 - 0.086 - Detritus 1.000 18369.530 - - 0.003 - 2009 Common carp 2.030 13.800 0.311 3.050 0.456 0.102 Black bass 3.698 0.001 1.774 3.590 0.009 0.494 Black bullhead (1+) 2.820 0.099 0.946 3.532 0.000 0.268 Black bullhead (0) 3.048 0.004 1.000 1.667 0.504 0.600 Bluegill (1+) 3.018 0.005 2.073 3.573 0.800 0.580 Bluegill (0) 3.035 0.001 2.200 3.667 0.677 0.600 Channel catfish 3.199 0.247 0.589 3.520 0.848 0.167 Other benthivores 2.634 0.000 2.054 3.606 0.045 0.570 Crappie 3.084 0.016 1.381 3.560 0.813 0.388 Flathead catfish 4.040 0.001 0.907 3.594 0.004 0.252 Darters 3.048 0.002 2.190 3.585 0.321 0.611 Esocids 4.066 0.001 0.789 3.594 0.000 0.219 Walleye (1+) 3.073 0.436 0.594 3.200 0.959 0.186

Group Trophic level B P/B Q/B EE P/Q Walleye (0) 3.197 0.029 14.130 23.550 0.017 0.600 White bass 3.225 0.140 0.809 3.527 0.193 0.229 Bigmouth buffalo 3.056 1.100 0.770 3.594 0.869 0.214 Minnows and shiners 3.050 0.022 1.819 3.552 0.262 0.512 Sunfish 3.030 0.001 1.000 3.594 0.838 0.278 Yellow perch 2.964 0.042 1.245 3.543 0.089 0.351 Yellow bass (0) 3.027 0.205 1.488 2.481 0.211 0.600 Yellow bass (1+) 3.056 2.500 1.200 3.200 0.762 0.375 Worms 2.063 0.820 2.113 7.043 0.782 0.300 Chironomidae 2.054 1.750 8.500 28.333 0.933 0.300 Amphipods 2.000 0.350 5.121 17.069 0.102 0.300 Benthic crustaceans 2.000 0.200 4.800 16.000 0.827 0.300 Benthic insects 2.010 0.232 5.600 18.667 0.152 0.300

Snails 2.000 0.087 3.530 11.767 0.173 0.300 152 Zebra mussels 2.000 7.217 5.000 16.667 0.922 0.300 Other bivalves 2.000 0.070 3.135 12.042 0.166 0.260 Copepod 2.116 0.500 4.400 12.571 0.736 0.350 Cladaceran 2.000 0.516 6.500 24.090 0.669 0.270 Rotifer 2.000 0.020 15.000 63.149 0.390 0.238 Benthic blue green 1.000 13.441 113.000 - 0.010 - Benthic Algae 1.000 4.490 113.000 - 0.086 - Planktonic blue green 1.000 190.856 85.000 - 0.002 - Planktonic Algae 1.000 2.777 113.000 - 0.353 - Macrophytes 1.000 0.363 9.000 - 0.532 - Detritus 1.000 15577.460 - - 0.002 - 2010 Common carp 2.031 16.900 0.385 3.487 0.136 0.110 Black bass 3.698 0.001 1.551 3.590 0.011 0.432 Black bullhead (1+) 2.820 0.200 1.099 3.551 0.007 0.309 Black bullhead (0) 3.048 0.015 1.700 2.833 0.343 0.600 Bluegill (1+) 3.018 0.008 1.403 3.565 0.835 0.394

Group Trophic level B P/B Q/B EE P/Q Bluegill (0) 3.035 0.001 2.100 3.500 0.880 0.600 Channel catfish 3.199 0.260 0.617 3.519 0.274 0.175 Other benthivores 2.634 0.069 0.916 3.537 0.008 0.259 Crappie 3.084 0.069 0.859 3.537 0.257 0.243 Flathead catfish 4.037 0.150 0.557 3.000 0.004 0.186 Darters 3.048 0.002 2.322 3.592 0.955 0.646 Esocids 4.066 0.084 0.612 3.534 0.000 0.173 Walleye (1+) 3.073 0.400 0.591 3.200 0.687 0.185 Walleye (0) 3.197 0.008 11.300 18.833 0.142 0.600 White bass 3.225 0.087 0.798 3.534 0.278 0.226 Bigmouth buffalo 3.053 1.300 0.595 3.532 0.506 0.168 Minnows and shiners 3.050 0.010 1.632 3.563 0.783 0.458 Sunfish 3.030 0.002 2.800 3.611 0.865 0.775

Yellow perch 2.964 0.044 1.196 3.543 0.111 0.338 153 Yellow bass (0) 3.027 0.104 1.750 2.917 0.381 0.600 Yellow bass (1+) 3.056 1.000 1.300 3.505 0.984 0.371 Worms 2.063 1.657 1.916 6.385 0.359 0.300 Chironomidae 2.054 2.291 5.200 17.333 0.965 0.300 Amphipods 2.000 0.063 5.700 19.000 0.469 0.300 Benthic crustaceans 2.000 0.437 3.853 12.844 0.497 0.300 Benthic insects 2.010 0.124 3.355 11.184 0.630 0.300 Snails 2.000 0.055 4.096 13.654 0.397 0.300 Zebra mussels 2.000 40.784 0.673 2.243 0.003 0.300 Other bivalves 2.000 0.135 3.135 12.042 0.153 0.260 Copepod 2.116 0.250 4.640 13.257 0.761 0.350 Cladaceran 2.000 0.280 5.100 18.901 0.963 0.270 Rotifer 2.000 0.013 7.500 31.575 0.757 0.238 Benthic blue green 1.000 13.525 113.000 - 0.009 - Benthic Algae 1.000 19.181 113.000 - 0.016 - Planktonic blue green 1.000 404.599 85.000 - 0.001 - Planktonic Algae 1.000 3.454 113.000 - 0.207 -

Group Trophic level B P/B Q/B EE P/Q Macrophytes 1.000 0.259 9.000 - 0.435 - Detritus 1.000 17432.330 - - 0.002 -

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155

CHAPTER 5. A SIMULATION APPROACH TO EVALUATE POTENTIAL NON-

NATIVE SPECIES IMPACTS ON WATER QUALITY AND FISHERY YIELD IN A

SHALLOW EUTROPHIC LAKE UNDERGOING RESTORATION

ABSTRACT

Degradation of water and habitat quality in many shallow Midwestern lakes has resulted in declining fishery yields and economic value of these important natural resources.

Although restoration activities in small, Midwestern lakes have been successful in improving water quality and local economies, the impacts of non-native species in an ecosystem context must be understood for continued restoration success. We developed the Clear Lake

Ecosystem Simulation Model (CLESM) to evaluate potential consequences of invading zebra mussel Dreissena polymorpha and established common carp Cyrinus carpio populations on water quality and recreational fishery yield dynamics in a shallow eutrophic lake undergoing restoration. Twenty-four scenarios were developed to evaluate potential consequences of non-native species in the context of restoration activities. Simulated water quality and recreational fishery yield dynamics had the largest response to changes in common carp biomass and harvest. Zebra mussel effects were dependent on common carp impacts. In particular, if common carp biomass increased beyond baseline levels, decreased water transparency limited phytoplankton production, thereby limiting zebra mussel production and impacts on the ecosystem. Specifically, zebra mussel impacts were negligible in scenarios of increased common carp biomass. However, simulations indicated that zebra mussel will positively impact water transparency if common carp biomass remains at baseline levels.

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Although, zebra mussel impacts were not wholly positive, increased recreational fishery

yield was observed in scenarios of increasing and decreasing zebra mussel biomass,

suggesting that food web impacts were both positive and negative. Marsh restoration effects

were negligible on water quality and recreational fishery dynamics, indicating that further

reduction of current loadings may not be as important as in-lake restoration to reduce internal

loading and other processes.

KEYWORDS

shallow lake restoration, common carp Cyrinus carpio, zebra mussel Dreissena

polymorpha, simulation modeling, water quality

INTRODUCTION

Non-native species directly and indirectly impact aquatic systems through trophic interactions and by otherwise inducing physical and chemical changes in the environment.

For example, common carp Cyprinus carpio, a ubiquitous non-native species in North

America, has been found to directly reduce macrophytes through consumption (Miller and

Crowl 2006; Miller and Provenza 2007). Additionally, foraging-induced suspension of

benthic sediment can reduce benthic light levels, thereby indirectly limiting macrophyte

abundance (Barko et al. 1991; Hinojosa-Garro and Zambrano 2004). In shallow lakes,

reduced macrophyte abundance can lead the lake to a turbid water state. This turbid water state is characterized by decreased water transparency and primary production dominated by phytoplankton rather than macrophytes, which are typically associated with a clear water state (Moss et al. 2002). Common carp effectively modify the lake light environment which is not accounted for in mass-balance food web models, thereby limiting the value of such

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models for evaluating potential non-native species consequences on ecosystem dynamics and

water quality. However, the effect of these environmental changes on ecosystem dynamics

can be simulated in a lake ecosystem model that goes beyond food web interactions.

Midwestern US lakes have experienced decades of elevated nutrient loading through

anthropogenic activities such as agriculture, leading to eutrophication, degraded water quality

through excessive phytoplankton production and decreased economic value (EPA 2000;

CARD 2008). Lake restoration can significantly improve water quality and positive effects

on local economies have been recognized (CARD 2008). In Iowa, for example, the Iowa

state legislature has allocated 2.3 to 10 million USD annually to fund lake restoration. Lake

restoration strategies focus on external (e.g., nutrient loading) and internal (e.g., nutrient

recycling) factors. Controlling external nutrient loading is a critical first step to lake

restoration, required to increase the likelihood of long term restoration (Meijer et al. 1998;

Phillips et al. 2003). Recognizing this, Iowa lake restoration policies prioritize external

nutrient and sediment loading reductions (i.e., watershed inputs), prior to in-lake actions.

In-lake restoration focuses on limiting nutrient recycling by benthivorous fish like

common carp. Common carp can have strong bottom-up control on phytoplankton

production through excretion and suspension of nutrient rich benthic sediments (Schrage and

Downing 2004). Bottom-up control of phytoplankton can be limited by manipulating

benthivorous fish biomass (i.e., biomanipulation). Within the Midwestern US, commercial

common carp removal is used as an in-lake restoration tool to limit biomass-dependent water

quality impacts since the early 1930s (Bailey and Harrison 1945; Rose and Moen 1953). The

combination of external and internal restoration actions should reduce lakes nutrient

availability, thereby limiting phytoplankton biomass. This in turn, should increase water

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column transparency, stimulating aquatic macrophyte production required to improve water

quality (Scheffer 1998; Moss et al. 2002).

Lakes that are undergoing restoration to limit phytoplankton production are also at

risk to invasion by filter-feeding (i.e., phytoplankton consuming) zebra mussels (Drake and

Bossenbroek 2004; Whittier et al. 2007). Zebra mussels are well known ecosystem engineers

that modify water column transparency (i.e., clear) by filter-feeding. Water column clearing

can increase the extent of a lake’s photic zone, indirectly stimulating benthic primary

production (e.g., macrophytes, benthic algae) (MacIsaac 1996; Zhu et al. 2006).

Additionally, excretion by large aggregations of zebra mussels can also alter nutrient cycles

and concentrations in lake ecosystems (Conroy et al. 2005). Zebra mussels may have strong,

yet uncertain effect on water quality and recreational fisheries because primary production is

the base of the food web in typical Midwestern shallow lakes. Ongoing restoration to limit

nutrient loading and recycling, coupled with potential zebra mussel invasion will likely effect

primary production in these systems, potentially leading to water quality and recreational

fishery changes.

Research on zebra mussel effects on water quality, recreational fisheries, and food

webs has focused on larger lakes (e.g., Great Lakes, Oneida Lake; MacIsaac et al. 1992;

Miehls et al. 2009). Published examples of zebra mussel impacts on smaller (<20 km2) eutrophic lentic systems are limited to a study on a stratified reservoir in Ohio (Yu and

Culver 1999; Yu and Culver 2000). Therefore, there is limited information available to assess potential impacts of zebra mussels in eutrophic lake ecosystems. Zebra mussel effects on water quality, fisheries, and the economy may be neutral or positive in such systems, where filter-feeding may increase water transparency (Effler et al. 1996; MacIsaac 1996;

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Budd et al. 2001). For example, a simulation study of potential zebra mussel effects on Lake

Mendota, Wisconsin, identified the potential for positive water quality benefits (Reed-

Andersen et al. 2000). Additionally, zebra mussels are used within their native range as a lake restoration tool, acting as biofilters to reduce phytoplankton concentrations, thereby increasing water clarity (Reeders and Devaate 1992; Orlova et al. 2004). However, potential zebra mussel impacts on water transparency may be nutrient dependent. In particular, phytoplankton assemblages in nutrient-rich systems typical of the Midwestern US can be dominated by low nutritional quality cyanobacteria (Arbuckle and Downing 2001; Downing et al. 2001c). Cyanobacterial dominance may limit zebra mussel production and potential water quality and recreational fisheries impacts. However, environmental conditions in which zebra mussel effects on water quality are positive or negative are poorly understood.

These non-native species have conflicting effects on water column transparency in shallow lakes. Common carp reduce, while zebra mussels may increase water column transparency. Additionally, lake restoration should limit nutrient availability and increase water column transparency by limiting phytoplankton production. The potential ecosystem impacts of these non-native species with ongoing restoration actions are difficult to empirically assess, since these dynamics have yet to occur. However, understanding these interactions to inform management is desired by lake managers to minimize adverse ecosystem and economic consequences. Simulation models are tools for assessing and forecasting system dynamics. These models can be used to understand complex ecosystem dynamics, and identify management strategies and counterintuitive system responses (e.g.,

Pine et al. 2009). Simulation models can be used to evaluate scenarios that would otherwise

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be impossible to evaluate due to prohibitively long time scales, excessive cost, or system

complexity.

One of the most popular and accessible aquatic ecosystem models currently in use is

ECOSIM, the time dynamic component of ECOPATH with ECOSIM (EwE) (Coll et al.

2009). ECOSIM simulations have been successfully used to inform management of

freshwater ecosystems. For example, Pine et al. (2007) used an ECOSIM model to evaluate

potential effects of an introduced apex predator (flathead catfish Pylodictis olivaris) in a

coastal riverine system. Although ECOSIM is primarily used to evaluate potential effects of fishing on ecosystems, it has also been used to evaluate non-native species impacts on ecosystems (e.g., Falk-Petersen 2004). While ECOSIM has emerged as a standard tool for

evaluating aquatic ecosystem dynamics, its capacity to evaluate consequences of non-native

species that can modify the lake environment is limited. In particular, the model does not

represent the effect of physical environmental changes (e.g., water transparency) on primary

production, which will likely be influenced by lake restoration, zebra mussels, and common

carp in shallow eutrophic lakes.

Shallow Midwestern lakes derive economic value from a combination of good water

quality for recreational and vacation use, and recreational fisheries. Evaluating potential

interactions of non-native ecosystem engineers and restoration on water quality and

recreational fisheries requires a holistic, integrative, systems modeling approach. In this chapter, a simulation model was developed for Clear Lake, Iowa (Clear Lake Ecosystem

Simulation Model, CLESM) by extending ECOSIM to account for physical environment and nutrient effects on primary production guided by the conceptual model presented in Figure 1.

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This model was then used to evaluate the potential impacts of common carp, zebra mussels,

and restoration on water quality and recreational fishery dynamics of Clear Lake.

METHODS

Study area

CLESM was developed for Clear Lake, a shallow (2.9 m mean depth) eutrophic lake

in north central Iowa (Figure 2). Clear Lake serves as a vacation and recreation destination

within Iowa. Economic value is estimated to be 43 million USD annually (CARD 2008),

with annual value of recreational fisheries exceeding 1 million USD (S. Grummer, Iowa

Department of Natural Resources (IADNR), personal communication). The lake is

windswept and well-mixed and stratification rarely occurs (Downing et al. 2001b).

Watershed restoration has reduced nutrient and sediment loadings to Ventura Marsh, the

major input to Clear Lake (D. Knoll Natural Resources Conservation Service, personal

communication). Clear Lake receives inputs from Ventura Marsh to the west and empties into Clear Creek on the eastern shore (Figure 2). Ventura Marsh inflow can be a significant source of nutrient and sediment inputs during periods of high benthivorous fish biomass

(Schrage and Downing 2004). A pump station was completed in 2011 to manage fish biomass in the marsh, allowing lake managers to reduce marsh water levels and create low dissolved oxygen conditions (i.e., fish mortality conditions).

Zebra mussels were initially detected in the system in 2005 and have rapidly colonized regions of the lake where firm substrates occur (~2% of the lake area). During the

2007 to 2010 period, zebra mussel biomass increased from 0.03 to 8 g•m-2. Biomass levels

in 2010 were approximately 5 times 2009 levels, indicating that population invasion may be

ongoing (M. Colvin unpublished data).

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Model structure

CLESM was developed to evaluate in-lake biotic and abiotic interactions; however

external factors (e.g., biomass import, nutrient loading) that play an important role in

ecosystem dynamics are represented as well (Figure 3). Six interrelated modules were used

to organize related CLESM inputs and equations, and provide output to related modules

(Figure 4). A conceptual model of shallow lake water quality was used to guide construction

in the software STELLA (www.iseesystems.com) (Figure 1). The following sections present general overviews of each module and governing equations. Detailed description of

equations are beyond the scope of this chapter, but see Appendices 3-8 for details.

Lake characteristics module

The lake characteristics module contains baseline lake physiochemical and

morphological values (e.g., retention time, mean lake depth, nutrient loadings). This module

provided output to the suspended sediment, nutrient, food web, and water quality modules

(Table 1). The model received inputs from the water quality and food web modules to

calculate the lake photic zone and macrophyte coverage, needed to modify the fraction of

lake susceptible to benthic sediment resuspension in the suspended sediment module.

Module details can be found in Appendix 3.

Food web module

The food web module is a time dynamic representation of the 2010 ECOPATH model

developed in Chapter 4, based on the ECOSIM model framework (Walters et al. 1997).

Change in biomass over time for food web group was simulated as:

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(1)

where, represents biomass gains from consumption of food items or primary production, represents the net biomass change from immigration (e.g., import, stocking) less export (e.g., emigration, flushing), represents biomass losses due to recreational and commercial fishing, represents the total biomass loss due to predator consumption, and is the net biomass losses due to unaccounted for processes, such as phytoplankton settling or biomass senescence. Rates and values required to parameterize Equation 1 for each food web group i were obtained from the

2010 ECOPATH model developed in Chapter 4, since future dynamics will depend on most recent system observation. The module accepts inputs from all modules except the suspended sediment module (Figure 4) (Table 1). Output (i.e., group-specific biomass) was provided to all modules. Module details can be found in Appendix 4.

Fishery module

Recreational and commercial fisheries are important components of the Clear Lake ecosystem. Changes in food web group-specific recreation and commercial fishery yield over time were simulated for food web group i as:

(2)

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where, is the sum of recreational and commercial fishery harvest of food web

group . The fishery module provides outputs (harvest amounts) and accepts inputs (biomass

to harvest) from the food web model (Table 1). Module details can be found in Appendix 5.

Nutrient module

Clear Lake to a eutrophic system characterized by high phytoplankton biomass due to

decades of nutrient loading. In particular phosphorus (P) limits primary production in this system (Downing et al. 2001b). Therefore, total phosphorous (TP) concentration in the water column was used link to phytoplankton production in the food web module. TP concentration is calculated as the sum of sestonic phosphorus components plus loading and excretion as:

(3)

where, is the phosphorus loading from Ventura Marsh, is the amount of P

excreted by food web groups, is the amount of P contained in suspended

sediments, is the amount of P contained in phytoplankton, and is the amount of P

contributed from external loading sources (e.g., groundwater). The nutrients module accepts

inputs from the suspended sediment, lake characteristics, and food web modules and provides

output to the food web (Table 1). Module details can be found in Appendix 6.

Suspended sediment module

Suspended sediment (refers collectively to organic and inorganic benthic material)

dynamics are an important determinant of water column transparency in shallow lakes

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(Scheffer 1998). Shallow lake suspended sediment () dynamics were simulated

using the system of ODEs:

(4)

where, is the suspended sediment concentration, is the amount of benthic sediment suspended, is the amount of suspended sediment imported to the system from Ventura Marsh, is the amount of lost due to settling,

is the amount lost to lake outflow, and is the amount of benthic sediment

available for suspension. The suspended sediment module uses inputs from the lake

characteristics and food web modules. Output from this module is provided to the nutrient

and water quality modules. Module details can be found in Appendix 7.

Water quality module

Water quality is commonly indexed by easily measured lake ecosystem components.

The water quality module calculates commonly used water quality indices, using inputs from

the nutrients, suspended sediments, and the food web module (Table 1). Total suspended solids (TSS) were calculated as plus phytoplankton dry weight from the suspended sediment and the food web modules respectively. Lake-specific models were then

used to estimate turbidity (Appendix 9) and Secchi transparency (Appendix 10) using module

inputs. The water quality module provided output to the food web and lake characteristics

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modules and received input from the food web, suspended sediment, and lake characteristics

modules (Table 1). Module details can be found in Appendix 8.

CLESM scenarios

Potential ecosystem impacts of non-native species and ongoing lake restoration are

uncertain and were evaluated using CLESM by simulating varying non-native species

biomass dynamics and restoration actions. Specifically, scenarios were developed to

evaluate external (marsh restoration) and internal (common carp harvest) restoration actions

in the context of varying non-native species biomass dynamics. Six classes of non-native

species biomass dynamics were developed: i) baseline (i.e., no change) common carp and

zebra mussel biomass, ii) baseline common carp and increasing zebra mussel biomass, iii)

baseline common carp and decreasing zebra mussel biomass, iv) increasing common carp

and baseline zebra mussel biomass, v) increasing common carp and zebra mussel biomass,

and vi) increasing common carp and decreasing zebra mussel biomass. Four internal and

external restoration scenarios were evaluated for each scenario class, scenario specifics

follow this section.

Varying non-native species biomass dynamics scenarios classes.—Scenarios were

developed to evaluate the effect of varying non-native species dynamics (i.e., baseline,

increasing, decreasing) in an ecosystem context by altering baseline other mortality rates

(Mo). Baseline values refer to mass-balance parameter estimates from the 2010 ECOPATH

model (see Chapter 4). Baseline values of Mo simulate equilibrium biomass (i.e., no change

in biomass over time). Increasing biomass was be simulated by decreasing food web group-

specific Mo from baseline values. Alternatively, decreasing biomass dynamics (i.e., population declines) can be simulated by increasing baseline Mo value for food web groups

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of interest. In scenario classes of increasing common carp biomass, Mo was decreased to a

value that in the absence of commercial fishing mortality resulted in biomass doubling time

of approximately 3 years based on the results of chapter 3. Zebra mussel Mo was decreased to simulate an 8 g/m2/year biomass, same as the increase observed between 2009 and 2010 in

scenario classes of increasing zebra mussel biomass. Scenario classes of decreasing zebra

mussel biomass were simulated by increasing Mo to simulate a 20% reduction in biomass

over the simulation period. Little is known about how rapidly zebra mussel biomass may

decline in response to adverse environmental conditions in eutrophic lakes; therefore this value was arbitrarily selected to simulate a hypothetical reduction. Baseline parameters of

food web groups excluding common carp and zebra mussels were unaltered during scenarios.

Ongoing restoration scenarios.— External loading reduction and limiting internal

cycling and resuspension of nutrients and sediments has been identified as lake restoration

actions for Clear Lake (Downing et al. 2001a). In particular, Ventura Marsh loading has

been identified as a manageable external loading source. Common carp biomass has been

identified as a significant contributor to internal nutrient cycling and sediment resuspension,

which can be managed by commercial harvest. Restoration scenarios were developed to evaluate the effect of external and internal restoration actions in context of the 6 scenario classes developed for non-native species biomass dynamics. Ongoing Ventura Marsh restoration was evaluated at baseline and a 30% reduction in loading of marsh inputs (i.e., phosphorus, sediment, phytoplankton). Commercial fishing mortality was evaluated assuming baseline and a 30% increase. Values were selected to represent a reasonable change in marsh loading and fishing mortality. Twenty four model scenarios were evaluated by simulating for all four restoration combinations for each scenario class. Extreme and

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intermediate values were not assessed to reasonably constrain simulations and computation

time. Scenario details can be found in Appendix 11.

Scenario implementation and evaluation.—Scenarios were simulated for 50 years,

since Iowa lake restoration legislation requires that water quality and public use benefits be

sustained over this period. Short term seasonal dynamics were not evaluated due to

computational limitations and the overall objective to evaluate long-term impacts.

Integrative metrics of water quality (i.e., Secchi transparency, TSS) and total recreational

fishery yield were used to evaluate system response to each scenario. Aquatic macrophytes and phytoplankton biomass are also significant components of shallow lake water quality and ecosystem dynamics and were therefore evaluated (Scheffer 1998). Ecosystem dynamics generated from baseline scenario classes provide a reference to compare system response to changing parameters. For example, the effect of increasing common carp biomass on the simulated ecosystem can be assessed by comparison to baseline dynamics. Model simulation outputs were transformed to relative values by dividing the simulation values by initial value at t=0 to facilitate among-scenario graphical comparison. Therefore, values of 1 reflect no

change from initial baseline conditions, values greater than or less than 1 reflect increasing or

decreasing values respectively.

RESULTS

Common carp biomass dynamics.—Relative common carp biomass dynamics varied

between scenarios classes simulating baseline and increasing common carp biomass (Figure

5). At baseline common carp and increasing zebra mussel biomass, common carp biomass

slightly increased. Dramatic effects on ecosystem dynamics were observed in the baseline

scenario class when commercial fishing mortality was increased and in scenarios classes

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simulating increasing common carp biomass. In baseline scenario classes, increased

commercial fishing mortality caused a decrease in biomass followed by an increase and finally, a decrease from initial values. Scenario classes increasing common carp biomass showed a steady increase in biomass over the simulation, regardless of changing Ventura

Marsh loading and zebra mussel biomass. Increased commercial fishing mortality did not

negate the overall pattern of increasing biomass; it simply decreased the magnitude of biomass increases in these scenarios classes. Common carp biomass dynamics differences were imperceptible for varying zebra mussel and Ventura Marsh scenarios.

Zebra mussel biomass dynamics.—Relative zebra mussel biomass dynamics varied among scenarios (Figure 6). Changes in zebra mussel biomass were negligible in the baseline scenario class. As expected, zebra mussel biomass increased or decreased in scenarios classes simulating baseline common carp biomass with increasing or decreasing zebra mussel biomass. Zebra mussel biomass declined in scenario classes simulating increased common carp biomass with baseline or decreasing zebra mussel biomass. The scenario class simulating both increasing common carp and zebra mussel biomass showed a gradual increase in zebra mussel biomass, followed by a decrease back to initial values over the simulation period. The effects of altered Ventura Marsh loading and commercial fishing mortality on zebra mussel biomass dynamics were slight or imperceptible in all scenario classes.

Water transparency dynamics.—Total suspended solids and Secchi transparency exhibited similar but opposite responses to scenarios evaluated (Figure 7 and Figure 8). Both metrics exhibited no change in baseline scenario classes, however increasing common carp commercial fishing mortality resulted in a decrease, followed by an increase, and finally a

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decrease in TSS concentration relative to baseline values. This patterns was similar but

opposite for Secchi transparency. A similar oscillation response in TSS and Secchi

transparency dynamics was observed in scenario classes simulating baseline common carp

biomass with increasing and decreasing zebra mussel biomass with increased commercial fishing mortality. TSS and Secchi transparency dynamics were similar (but in opposite directions) for scenario classes simulating common carp biomass increases and increased commercial fishing mortality decreased the magnitude of these responses within these scenario classes. The effect of reduced Ventura Marsh loading was imperceptible among all scenarios classes. Overall, scenarios simulated indicate that common carp biomass and commercial fishing mortality had the largest impact TSS and Secchi transparency dynamics, but zebra mussels can influence these dynamics if common carp biomass remains at baseline levels.

Phytoplankton dynamics.—Edible and inedible phytoplankton biomass dynamics were similar between comparable scenarios, but relative change was not as drastic for inedible phytoplankton (Figure 9 and Figure 10). Changes in edible and inedible phytoplankton biomass were imperceptible in this scenario class simulating baseline common carp and zebra mussel biomass; however an increase followed by a decrease biomass was observed when commercial fishing mortality was increased. Both edible and inedible phytoplankton biomass increased and decreased in scenario classes simulating baseline common carp biomass with increasing and decreasing zebra mussel biomass respectively.

Substantial decreases in both phytoplankton types were observed in scenario classes simulating increased common carp biomass. The effects of Ventura Marsh loading reduction

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were imperceptible among scenario classes and an effect of commercial fishing was only

discernible in the baseline scenario class.

Macrophyte dynamics.—Relative macrophyte biomass exhibited similar dynamics

between scenario classes simulating baseline and increased common carp biomass (Figure

11). In particular, dramatic biomass increases were observed when commercial fishing

mortality was increased in scenario classes simulating baseline common carp biomass. This

increase approached 50X initial biomass levels by year 20, followed by a decline back to

baseline biomass levels. Dynamics were similar among scenarios classes simulating

increased common carp biomass. Macrophyte biomass crashed to a fraction of initial levels

in short period of time (<10 years). Macrophytes dynamics were similar regardless of zebra

mussel or Ventura Marsh changes among all scenarios classes indicating that macrophyte

biomass was very sensitive to changes in common carp biomass, either through simulated increases or increased commercial fishing mortality.

Fishery yield dynamics.—Relative recreational fishery yield dynamics varied among

scenario classes (Figure 12). Relative fishery yield increased with increased commercial

fishing mortality in scenario classes simulating baseline common carp biomass with baseline

or decreasing zebra mussel biomass. Unexpectedly, recreational fishery yield increased in

the scenario class simulating decreasing zebra mussel biomass. Recreational fishery yield

increased in the scenario class simulating baseline common carp and increasing zebra mussel

biomass, with a similar response to increased commercial fishing mortality. Results were

similar among scenario classes simulating an increase in common carp biomass.

Recreational fishery yield declined over time and a small but positive effect of commercial

fishing was observed. The effects of reduced Ventura Marsh loading were imperceptible.

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DISCUSSION

This model and simulation results contribute to the limited understanding of non- native species impacts on water quality and recreational fishery yields in a eutrophic lake ecosystem undergoing restoration. Unlike the simulation model for Lake Mendota (Reed-

Andersen et al. 2000), where a zebra mussel invasion has yet to occur, this model is based on a eutrophic carp infested lake that has undergone an invasion. To my knowledge this model is the first attempt to couple the commonly used EwE framework with water quality and nutrient dynamics. Extending this existing food web model to incorporate additional abiotic and biotic interactions in a shallow lake ecosystem model was critical for a holistic view of the impact of non-native species and lake restoration on water quality and recreational fishery yields. However, lakes are complex ecosystems, rich with biotic and abiotic interactions. It is impossible to capture all of these interactions; therefore this model is a simplification of the ecosystem. CLESM provided unique insight into what might happen in terms of water quality and recreational fishery yield given current understanding. However, model forecasts are not certainty and simulated ecosystem changes should be used as a tool to further understand the system or to evaluate and screen potential restoration actions prior to implementation, utilizing a structured decision making or an adaptive resource management approach (Walters 1986; Williams et al. 2002).

Group-specific effects of common carp and zebra mussels on water quality in aquatic ecosystems have been well studied. However, the potential interaction of these two non- native species within the same ecosystem has received little attention. Both common carp and zebra mussels have caused dramatic ecosystem changes resulting from changes in water column transparency after introduction (e.g., Cahn 1929; MacIsaac 1996; Idrisi et al. 2001;

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Chumchal et al. 2005). This simulation study supports similar findings that zebra mussel impacts on the system will be positive in terms of water transparency (Fahnenstiel et al.

1995; Binding et al. 2007; Higgins et al. 2008). However in this system these positive effects

were limited by common carp biomass dynamics. Common carp negated positive zebra

mussel impacts on water transparency by limiting light required for phytoplankton

production, thereby limiting zebra mussel production. But, zebra mussels had limited impact

on common carp impact on common carp biomass dynamics. This is likely due to the

limited reliance of common carp on pelagic portions of the food web since they forage on

abundant benthic resources (i.e., chironomids, detritus). However, clearing of the water

column by zebra mussels can lead to increased benthic production (Lowe and Pillsbury 1995;

MacIsaac 1996; Gergs et al. 2009), which should stimulate common carp production.

However, this response was slight when water column transparency increased in the scenario

simulating baseline common carp and increasing zebra mussel biomass. Lack of a drastic

response by common carp to increased benthic production stimulated by zebra mussel

clearing of the water column is likely due to already existing super-abundant benthic food

resource within the lake, therefore a substantial change in benthic production and common

carp food resources would be required to illicit a response in common carp biomass

dynamics.

The responses of evaluated metrics to increased commercial fishing mortality in

baseline common carp scenario classes were unexpected. In particular, increased

commercial fishing mortality resulted in increased water clarity, which in turn stimulated

macrophyte production to approximately 50X baseline levels followed by a decline. This

was coupled with a decrease, followed by an increase in common carp biomass. Common

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carp positively responded to increased macrophyte biomass, since they are a component of

common carp diet (e.g., Miller and Crowl 2006). However this response was a lagged.

Common carp-macrophyte interactions are typically viewed negatively (i.e., common carp

negatively impact macrophytes). This simulation suggests that macrophytes can have a

positive but lagged effect on common carp biomass dynamics. This lagged response may be

a partial explanation for limited long-term shallow lake restoration success where increased

macrophyte biomass resulting from successful lake restoration provides additional food resources to common carp. However, this response requires substantial additional research to

determine if common carp-macrophyte dynamics occur as simulations suggest. Especially in

cases where increased macrophyte abundance can potentially lead to increased centrarchid

abundance, which can effectively suppress common carp through egg predation (Bajer et al.

2012). Another unexpected result of these simulations was the magnitude of macrophyte

response to in-lake restoration actions in baseline common carp scenario classes.

Macrophyte coverage occurs in a ring of shallow depths around the lake perimeter,

coinciding with dock areas and it is likely that macrophyte responses to successful lake

restoration may reach nuisance levels. Significant increases in aquatic macrophytes can

potentially limit recreational use and if ongoing restoration is successful, the next challenge

for Clear Lake may be aquatic plant management.

The response of phytoplankton to light limitation was severe for both edible and

inedible phytoplankton groups. Other mortality rates for phytoplankton groups were high

(i.e., >75% of annual production) reflecting excess production common in eutrophic systems.

The combination of high other mortality rates and additional light limitation resulted in

severe declines in phytoplankton production in scenario classes of increased common carp

175

biomass. This response was counterintuitive since high common carp biomass has been associated with phytoplankton increases (Chumchal et al. 2005). However, average phytoplankton biomass in Clear Lake during a period of high common carp biomass (i.e., early 2000s) was a fraction (~7%) of that observed in 2010 when carp biomass was less than

200 kg/ha (Iowa Lakes Information System 2005; Larscheid 2005; Colvin et al. 2010). It is likely that the model was simulating a reasonable response of phytoplankton to light limitation; however the magnitude of this effect may be overestimated. This suggests that key processes integrated in other mortality (e.g., settling) may overestimating the response of phytoplankton to resource limitation. For, example buoyancy of inedible phytoplankton likely limits settling losses (Kalff 2003); however this process was not accounted for in model simulations. Phytoplankton production in shallow eutrophic lakes is an important component of water quality and worthy of further research to better characterize dynamics.

The results of a simulation study by Reed-Andersen et al. (2000) noted that zebra mussel effects will likely be positive in Lake Mendota, Wisconsin, however whether negative outweigh positive impacts were unclear. Simulation results of this study suggest that if common carp biomass is sufficiently controlled, then zebra mussels can have a positive effect on water transparency and recreational fishery yield. However, this does not imply that zebra mussel effects were wholly beneficial to the system. In particular, simulated increases or decreases in zebra mussel biomass had positive effects on total recreational fishery yield.

This indicated that the energy required to support zebra mussels was negatively impacting the pelagic food web components supporting the recreation fishery. This is supported in scenarios simulating zebra mussel biomass decreases that resulted in an increase in recreational fishery yield. This result is likely explained by the shunting of from pelagic to

176

benthic regions of lake ecosystems energy by zebra mussels, which can alter food web

structure and stimulate benthic production (Miehls et al. 2009). Recreational harvest in Clear

Lakes contains a mixture of fish that feed on pelagic and benthic food items. Increased

benthic production may compensate for loss of pelagic energy required to support secondary

(i.e., zooplankton) and sport fish production thereby increasing recreational fishery yield

(Rutherford et al. 1999). Therefore, total recreational yield could respond positively to

shunting of energy to benthic portion of the food web in scenarios of increasing zebra mussel

biomass. Alternatively, reducing the benthic energy shunt by simulating decreasing zebra

mussel biomass would restore pelagic energy resources resulting in an increase in

recreational fishery yield as well.

Reducing external nutrient loading is the first step to successful shallow lake

restoration (Meijer et al. 1998; Sondergaard et al. 2007). Simulation results indicated that scenarios were insensitive to changes in baseline Ventura Marsh loading. However, these results are likely context dependent. For example, phytoplankton, TSS and TP levels for

Ventura Marsh were the lowest observed during the study period (M. Colvin unpublished data). This is likely due to Ventura Marsh water level reductions in 2010 to install a pump station. Reduced water levels limited aquatic habitat to a fraction of normal pool (M. Colvin personal observation), decreasing marsh volume and residence time. System flushing rate is related to residence time and increased flushing leads to decreased seston concentrations, thereby limiting phytoplankton, TSS and TP contributions to the lake (Lionard et al. 2008).

A limited response of in-lake water quality dynamics to changes in Ventura Marsh loading during simulations is not indicative that marsh contributions are unimportant to lake water quality; but that current seston concentrations are likely sufficiently low to have negligible

177

impact. Maintaining current Ventura Marsh water quality may effectively limit the potential

negative in-lake effects of marsh loading on lake sediment and nutrient budgets.

Recreational angling can significantly impact fish populations (Quist et al. 2011),

ecosystems (Kitchell et al. 2000), and economies, with recreational fishery value exceeding

300 million dollars annually in the state of Iowa (U.S. Department of the Interior 2006).

Novel or increased recreational fishing opportunities provided by changes in fish abundance

due to lake restoration or zebra mussel invasion may influence angler effort, which may have

ecosystem and economic consequences. For example, improved water quality may alter the

recreational yield from one dominated by walleye and yellow bass to sport fishes associated

with lower trophic states (e.g., centarchids, yellow perch) (Jeppesen et al. 2007). How

anglers respond to fishing opportunities resulting from water quality changes due to restoration ecosystem changes due to non-native species and restoration was not represented in this model. Changes in effort or switching behaviors of anglers in response to changes and the complex economics embodied therein are not represented in CLESM and therefore total yield results should be interpreted cautiously. In particular, system changes may alter sport fish abundance, which may result in additional fishing mortality or effort, which is unaccounted for in CLESM. Additional study would be required to adequately quantify angler dynamics and behavior in response to ecosystem changes and how these changes may impact the ecosystem.

ACKNOWLEDGMENTS

I acknowledge the efforts of Iowa Department of Natural Resources personnel: J.

Wahl, S. Gummer, J. Christenson, E. Thelen, D. Fjelt, J. Larschied, K. Bogenshutz, J

Euchner, and L. Fascher. I acknowledge the help of Iowa State University personal for

178

assisting in field work including V. Hengtes, Z. Jackson, J. Koch, T. Neebling, J. Lore, J.

Schmitz, C. Penne, D. Wooley, J. Norlin, G. Scholten, E. Kiefer, M. Sundberg, J. Skiff, and

K. Rebarchek. I am grateful to David Knoll for his assistance with this project. This project was supported in part by the Department of Natural Resource Ecology and Management at

Iowa State University, the Iowa Cooperative Fish and Wildlife Research Unit, and the Iowa

Department of Natural Resources. The use of trade names or products does not constitute endorsement by the U.S. Government.

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Figure 1. Conceptual model of important ecosystem components and likely interactions in Clear Lake, Iowa. Development of the quantitative simulation model CLESM was guided by this conceptual model.

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Ventura 4777000 Clear Lake Marsh

4776000

4775000 Northing

4774000

4773000 2 km N

460000 462000 464000 466000 468000

Easting

Figure 2. Map of Clear Lake, and location in north central Iowa. Horizontal arrows indicate inflow from Ventura Marsh and outflow at Clear Creek.

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Figure 3. Diagram of portions of the Clear Lake ecosystem considered to be within the boundary of CLESM (inside the dotted line) and factors considered to be independent of the ecosystem (outside the dotted line).

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Figure 4. Organization and coupling of CLESM modules. Modules (rectangles) provide inputs and accept outputs to model ecosystem and water quality dynamics. Double headed arrows illustrate modules that provide and receive outputs. External loadings represented in the model are shown as ovals. The dotted line corresponds to the system boundary in Figure 3.

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Scenario class: Baseline common carp and zebra mussel biomass 1.10 1.05 1.00 0.95 0.90 0.85 0.80

Scenario class: Baseline common carp and increasing zebra mussel biomass 1.10 1.05 1.00 0.95 0.90 0.85 0.80

Scenario class: Baseline common carp and decreasing zebra mussel biomass 1.10 1.05 1.00 0.95 0.90 0.85 0.80

Scenario class: Increasing common carp and baseline zebra mussel biomass 8 7 6 5 4 3 2 Relative common carp biomass carp common Relative 1

Scenario class: Increasing common carp and zebra mussel biomass 8 7 6 5 4 3 2 1

Scenario class: Increasing common carp and decreasing zebra mussel biomass 8 7 6 5 4 3 2 1 0 1020304050 Years Figure 5. Simulated common carp biomass dynamics for 24 scenarios in 6 scenario classes in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level. Black lines represent simulations with common carp commercial harvest remaining at baseline level; grey lines represent simulations with a 30% increase in commercial harvest mortality. Solid lines represent simulations with external loading remaining at baseline level; dashed lines represent a 30% reduction in external loading due to watershed restoration. Dashed lines are indistinguishable from solid lines in most plots due to negligible influence of external loading reduction. Note the difference in Y-axis scale between upper three and lower three plots.

191

Scenario class: Baseline common carp and zebra mussel biomass 1.10

1.05

1.00

0.95

0.90

Scenario class: Baseline common carp and increasing zebra mussel biomass 2.0 1.8 1.6 1.4 1.2 1.0 0.8

Scenario class: Baseline common carp and decreasing zebra mussel biomass 1.10 1.05 1.00 0.95 0.90 0.85 0.80

Scenario class: Increasing common carp and baseline zebra mussel biomass 1.2

1.1

1.0

0.9 Relativemussel biomass zebra 0.8

Scenario class: Increasing common carp and zebra mussel biomass 1.20

1.15

1.10

1.05

1.00

Scenario class: Increasing common carp and decreasing zebra mussel biomass 1.2

1.1

1.0

0.9

0.8 0 1020304050 Years Figure 6. Simulated zebra mussel biomass dynamics for 24 scenarios in 6 scenario classes in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level. Black lines represent simulations with common carp commercial harvest remaining at baseline level; grey lines represent simulations with a 30% increase in commercial harvest mortality. Solid lines represent simulations with external loading remaining at baseline level; dashed lines represent a 30% reduction in external loading due to watershed restoration. Dashed lines are indistinguishable from solid lines in most plots due to negligible influence of external loading reduction. Note the difference in Y-axis scale between upper three and lower three plots.

192

Scenario: Baseline common carp and zebra mussel biomass 1.2

1.1

1.0

0.9

0.8

Scenario class: Baseline common carp and increasing zebra mussel biomass 1.2 1.1 1.0 0.9 0.8 0.7

Scenario class: Baseline common carp and decreasing zebra mussel biomass 1.2

1.1

1.0

0.9

0.8

Scenario class: Increasing common carp and baseline zebra mussel biomass 10 8 6 4 2 Relative total suspended solids suspended total Relative 0

Scenario class: Increasing common carp and zebra mussel biomass 10 8 6 4 2 0

Scenario class: Increasing common carp and decreasing zebra mussel biomass 10 8 6 4 2 0

0 1020304050 Years Figure 7. Simulated total suspended solids dynamics for 24 scenarios in 6 scenario classes in Clear Lake, Iowa. Total suspended solids are shown as a proportion of baseline level. Black lines represent simulations with common carp commercial harvest remaining at baseline level; grey lines represent simulations with a 30% increase in commercial harvest mortality. Solid lines represent simulations with external loading remaining at baseline level; dashed lines represent a 30% reduction in external loading due to watershed restoration. Dashed lines are indistinguishable from solid lines in most plots due to negligible influence of external loading reduction. Note the difference in Y-axis scale between upper three and lower three plots.

193

Scenario class: Baseline common carp and zebra mussel biomass 1.10

1.05

1.00

0.95

0.90

Scenario class: Baseline common carp and increasing zebra mussel biomass 1.3

1.2

1.1

1.0

0.9

Scenario class: Baseline common carp and decreasing zebra mussel biomass 1.10

1.05

1.00

0.95

0.90

Scenario class: Increasing common carp and baseline zebra mussel biomass 1.2 1.0 0.8 0.6 0.4

Relative Secchi transparency 0.2 0.0

Scenario class: Increasing common carp and zebra mussel biomass 1.2 1.0 0.8 0.6 0.4 0.2 0.0

Scenario class: Increasing common carp and decreasing zebra mussel biomass 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0 1020304050 Years Figure 8. Simulated Secchi transparency dynamics for 24 scenarios in 6 scenario classes in Clear Lake, Iowa. Secchi transparency is shown as a proportion of baseline level. Black lines represent simulations with common carp commercial harvest remaining at baseline level; grey lines represent simulations with a 30% increase in commercial harvest mortality. Solid lines represent simulations with external loading remaining at baseline level; dashed lines represent a 30% reduction in external loading due to watershed restoration. Dashed lines are indistinguishable from solid lines in most plots due to negligible influence of external loading reduction. Note the difference in Y-axis scale between upper three and lower three plots.

194

Scenario class: Baseline common carp and zebra mussel biomass 1.2 1.1 1.0 0.9 0.8 0.7

Scenario class: Baseline common carp and increasing zebra mussel biomass 2.0

1.5

1.0

0.5

0.0

Scenario class: Baseline common carp and decreasing zebra mussel biomass 2.0

1.5

1.0

0.5

0.0

Scenario class: Increasing common carp and baseline zebra mussel biomass 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Relative edible biomass phytoplankton Scenario class: Increasing common carp and zebra mussel biomass 1.2 1.0 0.8 0.6 0.4 0.2 0.0

Scenario class: Increasing common carp and decreasing zebra mussel biomass 1.2 1.0 0.8 0.6 0.4 0.2 0.0 01020304050 Years Figure 9. Simulated edible phytoplankton biomass dynamics for 24 scenarios in 6 scenario classes in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level. Black lines represent simulations with common carp commercial harvest remaining at baseline level; grey lines represent simulations with a 30% increase in commercial harvest mortality. Solid lines represent simulations with external loading remaining at baseline level; dashed lines represent a 30% reduction in external loading due to watershed restoration. Dashed lines are indistinguishable from solid lines in most plots due to negligible influence of external loading reduction. Note the difference in Y-axis scale between upper three and lower three plots.

195

Scenario class: Baseline common carp and zebra mussel biomass 1.20 1.15 1.10 1.05 1.00 0.95 0.90

Scenario class: Baseline common carp and increasing zebra mussel biomass 1.2 1.1 1.0 0.9 0.8 0.7

Scenario class: Baseline common carp and decreasing zebra mussel biomass 1.20 1.15 1.10 1.05 1.00 0.95 0.90

Scenario class: Increasing common carp and baseline zebra mussel biomass 1.5

1.0

0.5

0.0

Relative inedible biomass phytoplankton Scenario class: Increasing common carp and zebra mussel biomass 1.5

1.0

0.5

0.0

Scenario class: Increasing common carp and decreasing zebra mussel biomass 1.5

1.0

0.5

0.0 01020304050 Years Figure 10. Simulated inedible phytoplankton biomass dynamics for 24 scenarios in 6 scenario classes in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level. Black lines represent simulations with common carp commercial harvest remaining at baseline level; grey lines represent simulations with a 30% increase in commercial harvest mortality. Solid lines represent simulations with external loading remaining at baseline level; dashed lines represent a 30% reduction in external loading due to watershed restoration. Dashed lines are indistinguishable from solid lines in most plots due to negligible influence of external loading reduction. Note the difference in Y-axis scale between upper three and lower three plots.

196

Scenario class: Baseline common carp and zebra mussel biomass 50 40 30 20 10 0 Scenario class: Baseline common carp and increasing zebra mussel biomass 50 40 30 20 10 0 Scenario class: Baseline common carp and decreasing zebra mussel biomass 50 40 30 20 10 0

Scenario class: Increasing common carp and baseline zebra mussel biomass 1.2 1.0 0.8 0.6 0.4

Relative macrophyte biomass macrophyte Relative 0.2 0.0

Scenario class: Increasing common carp and zebra mussel biomass 1.2 1.0 0.8 0.6 0.4 0.2 0.0

Scenario class: Increasing common carp and decreasing zebra mussel biomass 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0 1020304050 Years Figure 11. Simulated macrophyte dynamics for 24 scenarios in 6 scenario classes in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level. Black lines represent simulations with common carp commercial harvest remaining at baseline level; grey lines represent simulations with a 30% increase in commercial harvest mortality. Solid lines represent simulations with external loading remaining at baseline level; dashed lines represent a 30% reduction in external loading due to watershed restoration. Dashed lines are indistinguishable from solid lines in most plots due to negligible influence of external loading reduction. Note the difference in Y-axis scale between upper three and lower three plots.

197

Scenario class: Baseline common carp and zebra mussel biomass 1.2

1.1

1.0

0.9

0.8

Scenario class: Baseline common carp and increasing zebra mussel biomass 2.0 1.8 1.6 1.4 1.2 1.0 0.8

Scenario class: Baseline common carp and decreasing zebra mussel biomass 1.2

1.1

1.0

0.9

0.8

Scenario class: Increasing common carp and baseline zebra mussel biomass 1.5 Relative yield 1.0

0.5

0.0

Scenario class: Increasing common carp and zebra mussel biomass 1.5

1.0

0.5

0.0

Scenario class: Increasing common carp and decreasing zebra mussel biomass 1.5

1.0

0.5

0.0 0 1020304050 Years Figure 12. Simulated total recreational fishery yield dynamics for 24 scenarios in 6 scenario classes in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Black lines represent simulations with common carp commercial harvest remaining at baseline level; grey lines represent simulations with a 30% increase in commercial harvest mortality. Solid lines represent simulations with external loading remaining at baseline level; dashed lines represent a 30% reduction in external loading due to watershed restoration. Dashed lines are indistinguishable from solid lines in most plots due to negligible influence of external loading reduction. Note the difference in Y-axis scale between upper three and lower three plots.

198

Table 1. CLESM modules and descriptions of their outputs. Relationships among modules are shown in Figure 4. Details are shown in Appendices 4-9. Source module Receiving Output values Descriptions module Lake Nutrients External P loading Amount of P loading from characteristics external sources, excluding Ventura Marsh Lake depth Mean lake depth, required to calculate TP concentration Lake volume Required to calculate TP concentration TPVM Baseline TP concentration of Ventura Marsh outflow QVM Required to calculate total P loadings from marsh outflow RestorationVM Forcing function to simulate Ventura Marsh restoration, increases or reduces PVM values Suspended Lake depth Mean lake depth, required to sediment calculate TSS concentration TSSVM Baseline TSS concentration of Ventura Marsh outflow (2010 mean) Lake volume Volume of Clear Lake, required to calculate TSS concentration QVM Flow into the lake from Ventura Marsh Flushing rate Required to calculate flushing losses of TS Wind speed Average wind speed (2010 mean) Macrophyte Fraction of lake area with coverage macrophytes (%), modifies the amount of lake area susceptible to resuspension RestorationVM Forcing function to simulate Ventura Marsh restoration, increases or reduces TSSVM values Food web RestorationVM Forcing function to simulate Ventura Marsh restoration, alters biomass imported from Ventura Marsh for plankton

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Source module Receiving Output values Descriptions module groups (zoo- and phyto-) Water quality Lake volume required to convert phytoplankton biomass to a concentration Lake area required to convert phytoplankton biomass to a concentration Nutrient Food web TP required to calculate primary production rates Suspended Nutrient TSS required to calculate TP based sediment on predictive relationship in (Anthony et al. 2001) Water quality TSS required to calculate turbidity using a predictive model (Appendix 10) Water quality Food web Macrophyte Required to calculate biomass macrophyte coverage Lake Secchi Require to calculate photic characteristics transparency area Food web Fishery Biomassi Biomass of group i available for calculate harvested amount Nutrient Consumptioni Group specific total consumption, required to convert biomass consumed to an amount of P consumed Phytoplankton required to convert to P to biomass calculate TP concentration Water quality Phytoplankton Required to calculate Secchi biomass transparency Suspended Common carp Required to modify benthic sediment biomass resuspension rates Lake Macrophyte Required to calculate lake characteristics biomass area covered with macrophytes Fishery Food web Harvesti Amount of biomass harvested for group i by recreational and commercial fisheries

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CHAPTER 6. GENERAL CONCLUSIONS

There is a long use of models in fisheries and ecology. These models have varying assumptions of how underlying processes occur. Chapter 2 found that violating the assumption of continuous fishing could have consequences for managing harvest of fish populations. In particular, as more fish are harvested in a discrete rather than continuous fashion, parameter estimates of biomass dynamics models assuming continuous harvest can be biased. Accounting for discrete harvest using a semi-discrete biomass dynamics model reduced parameter bias and should be used when discrete harvest is occurring.

Extending the exponential semi-discrete biomass dynamics model to an actual population of common carp revealed that the population was increasing. Current levels of commercial harvest were insufficient to suppress biomass. Additionally, the rate of biomass increase was rapid, doubling in approximately three years in the absences of harvest. The effect of rapid biomass increase was further demonstrated by a sensitivity analysis of the model to unintentional underharvest. Sensitivity analyses results suggest the potential for a strong fisherman effect on common carp populations. A framework to assess the minimum amount of harvest needed to maintain common carp biomass was presented.

The effects of common carp and zebra mussels in aquatic food webs investigated in Chapter 4 revealed a potential impediment to lake restoration. In particular, zooplankton predation by age 0 yellow bass may limit top down regulation of phytoplankton biomass. Based on mass-balance estimates, age 0 yellow bass were estimated to consumer up to 50% of zooplankton production. The recent invasion of zebra mussel was shown to have an effect on phytoplankton populations, likely compensating for reduced zooplankton abundance. Common carp food web impacts were lower than expected, due to abundant benthic food resources.

The potential common carp and zebra mussel ecosystem impacts in the context of ongoing restoration were evaluated in Chapter 5. Simulated changes in common carp biomass had

201

dramatic effects on water quality and recreational fishery yield. This effect was due to increased suspended sediment reducing water transparency, which in turn limited phytoplankton production.

This also limited any effect of zebra mussels on water quality by limiting food resources (i.e., edible phytoplankton). In scenarios simulating baseline common carp biomass, increasing or decreasing zebra mussel biomass had positive and negative effects on water transparency respectively. Overall, zebra mussel impacts, positive or negative were limited to baseline common carp biomass scenario classes. Additionally, macrophytes biomass showed a dramatic increase when commercial fishing increased in scenarios of baseline common carp biomass, however this may lead to nuisance levels.

Unexpectedly, macrophyte increases in response to increased commercial fishing mortality also stimulated common carp increases as an increased food resource. This in turn reduced simulated water clarity and macrophyte biomass.

Results of this study suggest that in-lake processes are a significant component to water quality and recreational fishery yield. Controlling common carp biomass will critical to achieve water quality goals and minimize adverse effects on recreational fishery yield. The recent invasion of zebra mussels to the system will likely positively affect water clarity; however this will be limited by common carp. With common carp biomass controlled, zebra mussels can be expected to clear the water column; however this will reduce pelagic primary production, and require consumers to shift to feeding within benthic food web portions.

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APPENDIX 1: BIOTIC AND ABIOTIC SAMPLING

A significant component of this research was to quantify or index biomass of biotic

and abiotic components of Clear Lake. This supplemental information provides detailed

methods utilized to sample fish, zooplankton, benthic invertebrates, benthic algae,

phytoplankton, macrophytes, and detritus.

Benthic invertebrate

Sites were selected for benthic invertebrate sampling based on a stratified random

design. Sites were stratified by nearshore and offshore and substrate type (vegetated, mud,

sand, and rock). Benthic habitat was categorized as nearshore vegetated, sand, and mud

habitats and offshore mud and rocky habitats for a total of six strata. A total of 25 sites were

selected in Clear Lake (Figure 1). Macroinvertebrates inhabiting soft substrates (n=20) were

sampled using an Ekman grab lowered to the lake bed and activated from the water surface

(APHA 2005). Four dredge samples were taken at each soft substrate sampling location and

the collected material pooled (total area sampled=0.20 m2). A dome suction sampler was

used to collect macroinvertebrates from a 0.20 m2 horizontal benthic surface area where

inorganic substrate was dominated by gravel or rock (n=10) during mid to late July 2007–

2010 (APHA 2005; Stewart and Haynes 1994). The suction sampler was operated at the sampling location for three minutes by SCUBA divers (Stewart and Haynes 1994). Surficial

substrate (to sediment depth of 2.5 cm) was examined for zebra mussels at the conclusion of

the three-minute sampling period. Substrate was transferred to plastic bags by hand picking

and a trowel and the contents transported to the water surface. If rocks are embedded in the

lakebed or too large to move, divers used their hands to locate and remove attached mussels 203

and transfer them to plastic bags. The collected materials were processed through a 500 µm mesh bucket sieve with filtered lake water. The substrate sample retained in the bucket sieve was transferred to a 1 gallon plastic jar with a solution of 10% buffered formalin and Rose

Bengal dye (APHA 2005). Formalin was replaced with 70% alcohol within 48 h of returning to the laboratory (APHA 2005).

Benthic samples were spread on a gridded tray and rare/large bodied macro invertebrate taxa (e.g., large zebra mussels, crayfish) search was performed for a total of five minutes

(King and Richardson 2002). Encountered large bodied macroinvertebrates were placed in a vial of 70% ethanol for later identification. After searching for rare and large bodied organisms, the benthic sample was homogenized in a gridded tray and grid cells were randomly selected and macroinvertebrates completely removed under 10X magnification until a minimum of 500 individuals are removed and at least two grid cells were sampled

(USEPA 2004). Macroinvertebrates were sorted into functional groupings and dried for 48 h at 60C and weighed.

Biomass (g dry weight/m2) was estimated by stratified sampling to account for taxa differences amount major benthic habitat types in the lake. Mean biomass was estimated by summing weighted within stratum means for each biotic group. Confidence intervals (95%) of biomass estimates were estimated by bootstrap resampling (Efron and Tibshirani 1991).

Benthic algae and detritus biomass

An Ekman grab was used to sample inorganic soft substrate (e.g., mud or sand) during 17-19 July 2007 and 9-10 July 2008. Three equal-sized aliquots of the top 8 mm of sediment from each grab were collected by pushing a Petri dish lid into the sediment surface and sliding a spatula underneath (55 mm diameter, 172.7 mm2) (Potapova and Charles 2005). 204

The three aliquots were transferred to separate opaque bottles. Two bottles were frozen on

dry ice for laboratory analysis of chlorophyll a (i.e., total benthic algal biomass), and

abundance of particulate organic matter (POM) (APHA 2005).

Surficial substrate (top 5 mm) was collected from within a 0.05 m2 frame at four

randomly determined locations for sampling locations dominated by gravel or rocks during

25-26 July 2007 (total sampling area = 0.2 m2). This substrate was transported to the water

surface in plastic bags. At the water surface, algae and other particulate matter were removed

from gravel and rocks by placing the collected material in a bucket, and scrubbing and

rinsing substrate with a soft-bristled toothbrush and tap water (APHA 2005). Algae and other

organic matter were then separated from most inorganic material by mixing bucket contents

and pouring the liquid component (supernatant) into a separate bucket. The liquid

algae/organic matter sample was mixed and three aliquots of this slurry were transferred to separate opaque bottles. Two bottles were frozen on dry ice for laboratory analysis of chlorophyll a (i.e., total benthic algal biomass), and abundance of non-algal organic matter

(CPOM) (APHA 2005).

Each chlorophyll a sample was thawed in the laboratory and filtered through a glass fiber filter to concentrate algae (47 µm glass filter) (APHA 2005). Filters were frozen and sent to the University of Iowa Hygienics Laboratory (UHL) for determination of total algal biomass (APHA 2005). Benthic organic matter mass (g AFDW cm-2 horizontal benthic

surface area) were determined using standard methods for drying and ashing organic matter

(APHA 2005).

Detritus, benthic algae, and benthic macro fauna (> 500 µm) biomass was estimated

based on the stratified random sampling design used to collect the samples. Mean strata 205

biomasses for functional groups were multiplied by the strata weight and summed for a lake-

wide estimate of biomass (Cochran 1963; Thompson 2002). Associated variances and

confidence intervals were estimated based estimators for a stratified random sample.

Fish

Common carp.—Mean annual common carp biomass was estimated using mark- recapture and a semi-discrete biomass dynamics model (see Chapter 3).

Nearshore fish assemblage.—The nearshore fish assemblage was sampled during

September 2007-2010. Sites selected for seining were assumed to be representative of the nearshore fish assemblage. Sites could not be selected at random due to the presence of docks and structures that precluded the use of the seine. Sites were selected to establish seven index sites that were seined using a 132 m seine that is 3.5 m in depth with a 3.5 m x

3.5 m bag in center and 6 mm mesh (Figure 2). The seine had a weighted bottom line, floats along the top line, and was deployed from a boat in a semicircle extending out from the shoreline. Both ends of the seines were pulled to shore simultaneously (Liao et al. 2004).

The seine sampled 0.28 ha when set in a semicircular fashion. Seining was conducted during the day due to safety and manpower considerations. It was assumed that high lake turbidity during the sample period minimizes seine evasion by fish. Captured fish were identified, enumerated, measured (nearest 1 mm), and weighed. Numerically abundant age 0 fish (e.g., juvenile yellow bass) were counted and weighed in bulk for biomass estimates.

Offshore fish assemblage.—The offshore fish assemblage was sampled by bottom trawling during the end of September and early October of 2007-2010. A semi-balloon otter trawl with a 8 m head rope, 3.8 cm stretch mesh body, and 6.3 mm mesh cod end was used to 206

sample the offshore fish community (Larscheid 2005). The trawl was towed at a speed of

3.2-4 km hour-1 for a period of 5 minutes (Larscheid 2005). Starting locations for trawling routes were selected at random from locations constrained to be greater than 100 m from shore and at least 400 m from the nearest starting location. Trawls were allocated proportionally to the area of the three lake basins (Little = 4, Middle = 18, Big = 22) for a total of 45 sites (Figure 2). The trawling route direction (i.e., bearing) was randomly selected. Coordinates for the trawl route were recorded at 30 second intervals using a handheld Global Positioning System (GPS) and used to calculate the linear distance sampled using a geographic information system (GIS). The area sampled by the trawl was estimated by multiplying the linear distance sampled by the width of the trawl (5.72 m). Captured fish were identified, enumerated, and weighed. A representative subsample were measured and weighed for numerically abundant species.

Plankton

Water samples were taken at three lake locations distributed among the three major basins of the lake (Figure 3). An additional water sampling location was located at the lake inflow located between Ventura Marsh and Clear to quantify the amount of zooplankton and phytoplankton entering the lake. Sampling was conducted during the lake ice-free period

(April–October). A 53 µm zooplankton net was used to sample zooplankton. A zooplankton net was towed vertically through the water column and contents rinsed into a sample bottle.

Zooplankton were anesthetized using sodium bicarbonate (i.e., Alka Seltzer) and fixed with

70% ethanol. Zooplankton and phytoplankton samples were identified and biomass estimated by University Hygienic Laboratory (Ankeny, IA) using standard methodologies. A 207

contract mishap precluded project-specific sampling in 2007. Mean zooplankton and

phytoplankton annual biomass was therefore calculated using biomass data acquired from

Iowa Lakes Information System (2005). Mean within year phytoplankton and zooplankton

biomass (mg wet weight/L) was multiplied by lake volume (4.29 x 1010 L) to estimate whole

lake biomass. Whole lake biomass was then divided by lake area (14.64 km2) and used as

biomass input to year-specific ECOPATH models.

Macrophytes

The biomass of aquatic vegetation was estimated from vegetation surveys conducted

by IDNR. Vegetation surveys calculate macrophyte coverage and diversity at 17 transects

systematically placed around the lake (Figure 4) (Quist et al. 2007).

LITERATURE CITED

APHA, (American Public Health Association). 2005. Standard methods for the examination of water and wastewater, 21 edition. American Public Health Association, Washington, DC.

Cochran, W. G. 1963. Sampling Techniques, 2nd edition, NY.

Efron, B., and R. Tibshirani. 1991. Statistical data analysis in the computer age. Science 253:390‐395.

Iowa Lakes Information System. 2005. Iowa Lakes Information System. http://limnology.eeob.iastate.edu/lakereport/.

King, R. S., and C. J. Richardson. 2002. Evaluating subsampling approaches and macroinvertebrate taxonomic resolution for wetland bioassessment. Journal of the North American Benthological Society 21:150‐171.

Larscheid, J. G. 2005. Population densities, biomass and age‐growth of common carp and black bullheads in Clear Lake and Ventura Marsh. Iowa Department of Natural Resources Report F‐160‐R, Des Moines.

Liao, H., C. L. Pierce, and J. G. Larscheid. 2004. Consumption dynamics of the adult piscivorous fish community in Spirit Lake, Iowa. North American Journal of Fisheries Management 24:890‐902. 208

Potapova, M., and D. F. Charles. 2005. Choice of substrate in algae‐based water‐quality assessment. Journal of the North American Benthological Society 24:415‐427.

Quist, M. C., L. Bruce, K. Bogenschutz, and J. E. Morris. 2007. Sample size requirements for estimating species richness of aquatic vegetation in Iowa Lakes. Journal of Freshwater Ecology 22:477‐491.

Stewart, T. W., and J. M. Haynes. 1994. Benthic macroinvertebrate communities of southwestern lake‐ontario following invasion of dreissena. Journal of Great Lakes Research 20:479‐493.

Thompson, S. K. 2002. Sampling, 2nd edition, NY.

USEPA. 2004. Wadeable stream assessment: benthic laboratory methods. U.S. Environmental Protection Agency, Office of Water and Office of Research and Development Washington DC.

209

Ventura Little Middle and Big 4778000 Marsh Lake Clear Lake

4776000 Northing 4774000

4772000 Marsh Sand Mud Rock and Gravel 4770000 Emergent Vegetation N

460000 462000 464000 466000 468000

Easting

Figure 1. Benthic invertebrate sampling locations. Sites were stratified among mud, sand, vegetated, and hard substrates. Mud and hard substrate stratas were further stratified into nearshore and offshore sites. A total of 25 sites were sampled on Clear Lake.

210

4776500 A: 2007 4776000 4775500 4775000 4774500 4774000 4773500 4773000

4776500 B: 2008 4776000 4775500 4775000 4774500 4774000 4773500 4773000

4776500 C: 2009

Northing 4776000 4775500 4775000 4774500 4774000 4773500 4773000

4776500 D: 2010 Seine location 4776000 Traw l location 4775500 4775000 4774500 4774000 4773500 N 4773000

458000 460000 462000 464000 466000 468000 470000 Easting

Figure 2. Nearshore and offshore fish assemblage sampling locations.

211

Ambient Lakes Monitoring: Monthly (Apr, Jul, Sep) ISU Lakes Survey: Monthly (Jun-Aug) DNR Fisheries Lakes Program: Twice Monthly (Apr-Oct)

Ventura Little Middle and Big 4778000 Marsh Lake Clear Lake

4776000 Northing 4774000

4772000

4770000 N

460000 462000 464000 466000 468000

Easting

Figure 3. Water quality sampling locations. 212

IADNR Vegetation Transect Locations 2006-2009

4778000

4776000 Northing 4774000

4772000

4770000 N

460000 462000 464000 466000 468000

Easting

Figure 4. Macrophyte sampling locations.

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APPENDIX 2: ECOPATH BASIC INPUTS

Table 1. Groupings of Clear Lake taxa for ECOPATH modeling. ECOPATH Group Taxa Common carp Cyprinus carpio Black bass Micropterus salmoides Black bullhead (1+) Ameiurus melas Black bullhead (0) Ameiurus melas Bluegill (1+) Lepomis macrochirus Bluegill (0) Lepomis macrochirus Channel catfish Ictalurus punctatus Other benthivores Noturus gyrinus Catostomus commersonii Crappie Pomoxis nigromaculatus P. annularis Flathead catfish Pylodictis olivaris Darters Etheostoma nigrum Percina caprodes Esocids Esox masquinongy Esox lucius Walleye (1+) Sander vitreus Walleye (0) Sander vitreus White bass Morone chrysops Bigmouth buffalo Ictiobus cyprinellus Minnows and shiners Notemigonus crysoleucas Notropis hudsonius Pimephales promelas Sunfish Lepomis spp. Yellow perch Perca flavescens Yellow bass (0) Morone mississippiensis Yellow bass (1+) Morone mississippiensis Benthic crustaceans (amphipoda) Amphipoda: Amphipoda Benthic crustaceans (decapods) Decapoda: Decapoda Benthic crustaceans (non amphipoda) Cladacera: Cladaceran Cladacera: Bosmina Cyclopoida: Cyclopoida Calanoida: Calanoida Harpacticoida: Harpacticoida Isopoda: Asellidae Ostracoda: Ostracod 214

ECOPATH Group Taxa Benthic insects Trombidformes: Hydracarina Coleoptera: Haliplidae Diptera: Chaoboridae Diptera: Ceratopogonidae Ephemeroptera: Caenidae Megaloptera: Sialidae Trichoptera: Leptoceridae Trichoptera: Helicopsychidae Trichoptera: Hydroptilidae Lepidoptera: Pyralidae Chironomidae Diptera: Chironomidae Dreisseniidae Veneroida: Dreissenidae Flatworms (Turbellaria) Turbellaria Leeches (Hirudinea) Hirudinea Other bivalves Veneroida: Sphaeriidae Snails Gastropoda: Ancylidae Architaenioglossa : Viviparidae Heterostropha: Valvatidae Sorbeoconcha: Hydrobiidae Pulmonata: Physidae Pulmonata: Planorbidae Worms Oligochaeta Nematoda Detritus -

Table 2. Basic inputs for annual ECOPATH models. Group B P/B Q/B Import Export Stocking BA Ycom Yrec 2007 Common carp 3.352 0.409 3.485 0.000 0.000 0.000 0.39 5.610 0.000 Black bass 0.007 0.960 3.567 0.000 0.000 0.000 0.00 0.000 0.000 Black bullhead (1+) 0.426 0.732 3.512 0.000 0.000 0.000 -0.29 0.000 0.000 Black bullhead (0) 0.156 1.449 3.526 0.000 0.000 0.000 -0.15 0.000 0.000 Bluegill (1+) 0.023 1.123 3.552 0.000 0.000 0.000 -0.02 0.000 0.000 Bluegill (0) 0.003 2.271 3.579 0.000 0.000 0.000 0.01 0.000 0.000 Channel catfish 0.152 0.683 3.526 0.000 0.000 0.023 -0.08 0.000 0.070 Other benthivores 0.296 0.661 3.517 0.000 0.000 0.000 -0.30 0.000 0.000 Crappie 0.043 0.948 3.543 0.000 0.000 0.000 -0.03 0.000 0.005 Flathead catfish 0.365 0.651 3.514 0.000 0.000 0.000 1.39 0.000 0.000 Darters 0.008 1.846 3.566 0.000 0.000 0.000 -0.01 0.000 0.000

Esocids 0.216 0.537 3.521 0.000 0.000 0.003 -0.05 0.000 0.000 215 Walleye (1+) 0.791 0.517 3.504 0.000 0.000 0.000 -0.58 0.000 0.585 Walleye (0) 0.040 1.124 3.544 0.000 0.000 0.442 -0.03 0.000 0.000 White bass 0.123 0.729 3.529 0.000 0.000 0.000 0.21 0.000 0.052 Bigmouth buffalo 1.501 0.454 3.496 0.000 0.000 0.000 1.40 0.216 0.000 Min. and shiners 0.016 1.692 3.557 0.000 0.000 0.000 -0.01 0.000 0.000 Sunfish 0.001 1.393 3.595 0.000 0.000 0.000 0.00 0.000 0.009 Yellow perch 0.022 1.120 3.552 0.000 0.000 0.000 -0.01 0.000 0.004 Yellow bass (0) 0.075 1.179 3.536 0.000 0.000 0.000 0.02 0.000 0.000 Yellow bass (1+) 0.887 0.759 3.503 0.000 0.000 0.000 0.29 0.000 0.728 Worms 4.341 1.229 4.097 0.000 0.000 0.000 -2.75 0.000 0.000 Chironomidae 21.633 0.797 2.655 0.000 0.000 0.000 -13.25 0.000 0.000 Amphipods 0.010 6.340 21.132 0.000 0.000 0.000 0.05 0.000 0.000 Ben. Crust. 2.528 1.422 4.741 0.000 0.000 0.000 -1.39 0.000 0.000 Benthic insects 0.221 2.745 9.151 0.000 0.000 0.000 -0.12 0.000 0.000 Snails 0.051 4.073 13.575 0.000 0.000 0.000 -0.04 0.000 0.000 Zebra mussels 0.199 2.826 9.421 0.000 0.000 0.000 6.93 0.000 0.000 Other bivalves 0.103 3.376 11.253 0.000 0.000 0.000 -0.06 0.000 0.000

Group B P/B Q/B Import Export Stocking BA Ycom Yrec Copepod 0.590 2.212 8.507 0.365 0.212 0.000 0.16 0.000 0.000 Cladaceran 0.665 2.142 8.237 0.148 0.212 0.000 0.15 0.000 0.000 Rotifer 0.009 6.760 26.000 0.119 0.212 0.000 0.60 0.000 0.000 Benthic blue green 8.571 113.000 0.000 0.000 0.000 0.000 -7.96 0.000 0.000 Benthic Algae 14.430 113.000 0.000 0.000 0.000 0.000 -13.85 0.000 0.000 Planktonic blue green 108.516 85.000 0.000 28.753 0.212 0.000 65.69 0.000 0.000 Planktonic Algae 1.834 113.000 0.000 5.865 0.212 0.000 5.77 0.000 0.000 Macrophytes 0.259 7.727 0.000 0.000 0.000 0.000 0.30 0.000 0.000 Detritus 14357.154 0.000 0.000 0.000 0.000 0.000 - 0.000 0.000 2008 Common carp 3.743 0.39 3.484 0.000 0.000 0.000 -0.36 5.809 0.000 Black bass 0.005 1.05 3.573 0.000 0.000 0.000 0.00 0.000 0.000 Black bullhead (1+) 0.138 0.83 3.528 0.000 0.000 0.000 -0.04 0.000 0.000

Black bullhead (0) 0.001 1.00 3.594 0.000 0.000 0.000 0.00 0.000 0.000 216 Bluegill (1+) 0.002 1.63 3.588 0.000 0.000 0.000 0.00 0.000 0.000 Bluegill (0) 0.009 2.25 3.564 0.000 0.000 0.000 0.01 0.000 0.000 Channel catfish 0.070 0.71 3.537 0.000 0.000 0.044 0.18 0.000 0.051 Other benthivores 0.001 2.44 3.594 0.000 0.000 0.000 0.00 0.000 0.000 Crappie 0.012 1.07 3.560 0.000 0.000 0.000 0.00 0.000 0.013 Flathead catfish 1.757 0.44 3.494 0.000 0.000 0.000 -1.76 0.000 0.000 Darters 0.002 2.06 3.584 0.000 0.000 0.000 0.00 0.000 0.000 Esocids 0.166 0.63 3.525 0.000 0.000 0.000 -0.17 0.000 0.000 Walleye (1+) 0.214 0.59 3.522 0.000 0.000 0.000 0.12 0.000 0.630 Walleye (0) 0.007 1.50 3.568 0.000 0.000 0.388 0.02 0.000 0.000 White bass 0.336 0.57 3.516 0.000 0.000 0.000 -0.20 0.000 0.045 Bigmouth buffalo 2.898 0.41 3.487 0.000 0.000 0.000 -2.90 0.923 0.000 Min. and shiners 0.002 2.07 3.587 0.000 0.000 0.000 0.02 0.000 0.000 Sunfish 0.000 3.10 3.624 0.000 0.000 0.000 0.00 0.000 0.002 Yellow perch 0.010 1.19 3.563 0.000 0.000 0.000 0.03 0.000 0.002 Yellow bass (0) 0.090 1.02 3.533 0.000 0.000 0.000 0.59 0.000 0.000 Yellow bass (1+) 1.177 0.76 3.499 0.000 0.000 0.000 -0.62 0.000 1.352

Group B P/B Q/B Import Export Stocking BA Ycom Yrec Worms 1.591 1.61 5.372 0.000 0.000 0.000 -1.01 0.000 0.000 Chironomidae 8.382 1.03 3.430 0.000 0.000 0.000 -7.57 0.000 0.000 Amphipods 0.062 3.88 12.918 0.000 0.000 0.000 -0.04 0.000 0.000 Ben. Crust. 1.136 1.77 5.884 0.000 0.000 0.000 -0.96 0.000 0.000 Benthic insects 0.104 3.36 11.206 0.000 0.000 0.000 0.01 0.000 0.000 Snails 0.015 5.68 18.927 0.000 0.000 0.000 0.07 0.000 0.000 Zebra mussels 7.125 1.08 3.584 0.000 0.000 0.000 0.09 0.000 0.000 Other bivalves 0.044 4.24 14.121 0.000 0.000 0.000 0.03 0.000 0.000 Copepod 0.750 2.07 7.972 0.266 0.212 0.000 -0.53 0.000 0.000 Cladaceran 0.813 2.03 7.802 0.147 0.212 0.000 -0.30 0.000 0.000 Rotifer 0.604 2.20 8.451 0.089 0.212 0.000 -0.58 0.000 0.000 Benthic blue green 0.615 113.00 0.000 0.000 0.000 0.000 12.83 0.000 0.000 Benthic Algae 0.580 113.00 0.000 0.000 0.000 0.000 3.91 0.000 0.000

Planktonic blue green 174.208 85.00 0.000 14.585 0.212 0.000 16.65 0.000 0.000 217 Planktonic Algae 7.604 113.00 0.000 6.643 0.212 0.000 -4.83 0.000 0.000 Macrophytes 0.259 7.73 0.000 0.000 0.000 0.000 0.30 0.000 0.000 Detritus 18369.528 0.00 0.000 0.000 0.000 0.000 0.000 0.000 2009 Common carp 3.386 0.392 3.485 0.000 0.000 0.000 -0.51 1.956 0.000 Black bass 0.001 1.774 3.590 0.000 0.000 0.000 0.00 0.000 0.000 Black bullhead (1+) 0.099 0.860 3.532 0.000 0.000 0.000 -0.08 0.000 0.000 Black bullhead (0) 0.001 1.000 3.594 0.000 0.000 0.000 0.00 0.000 0.000 Bluegill (1+) 0.005 1.727 3.573 0.000 0.000 0.000 0.00 0.000 0.000 Bluegill (0) 0.018 2.041 3.555 0.000 0.000 0.000 0.00 0.000 0.000 Channel catfish 0.247 0.589 3.520 0.000 0.000 0.000 0.01 0.000 0.123 Other benthivores 0.000 2.054 3.606 0.000 0.000 0.000 0.03 0.000 0.000 Crappie 0.012 1.255 3.560 0.000 0.000 0.000 0.06 0.000 0.017 Flathead catfish 0.001 0.756 3.594 0.000 0.000 0.000 0.23 0.000 0.000 Darters 0.002 2.190 3.585 0.000 0.000 0.000 0.00 0.000 0.000 Esocids 0.001 0.789 3.594 0.000 0.000 0.011 0.08 0.000 0.000 Walleye (1+) 0.335 0.594 3.516 0.000 0.000 0.000 0.20 0.000 0.247

Group B P/B Q/B Import Export Stocking BA Ycom Yrec Walleye (0) 0.029 1.126 3.548 0.000 0.000 0.416 0.00 0.000 0.000 White bass 0.140 0.809 3.527 0.000 0.000 0.000 -0.05 0.000 0.017 Bigmouth buffalo 0.001 0.708 3.594 0.000 0.000 0.000 0.09 0.706 0.000 Min. and shiners 0.022 1.819 3.552 0.000 0.000 0.000 -0.01 0.000 0.000 Sunfish 0.001 1.000 3.594 0.000 0.000 0.000 0.00 0.000 0.001 Yellow perch 0.042 1.245 3.543 0.000 0.000 0.000 0.00 0.000 0.002 Yellow bass (0) 0.685 1.488 3.506 0.000 0.000 0.000 -0.05 0.000 0.000 Yellow bass (1+) 0.557 0.797 3.509 0.000 0.000 0.000 0.16 0.000 2.664 Worms 0.583 2.113 7.043 0.000 0.000 0.000 1.03 0.000 0.000 Chironomidae 0.815 1.931 6.435 0.000 0.000 0.000 1.46 0.000 0.000 Amphipods 0.022 5.129 17.096 0.000 0.000 0.000 0.04 0.000 0.000 Ben. Crust. 0.178 2.911 9.703 0.000 0.000 0.000 0.26 0.000 0.000 Benthic insects 0.111 3.306 11.019 0.000 0.000 0.000 -0.01 0.000 0.000

Snails 0.087 3.530 11.767 0.000 0.000 0.000 -0.04 0.000 0.000 218 Zebra mussels 7.217 1.071 3.572 0.000 0.000 0.000 33.19 0.000 0.000 Other bivalves 0.070 3.749 12.497 0.000 0.000 0.000 0.07 0.000 0.000 Copepod 0.223 2.876 11.060 0.409 0.212 0.000 -0.08 0.000 0.000 Cladaceran 0.516 2.293 8.820 0.164 0.212 0.000 -0.30 0.000 0.000 Rotifer 0.020 5.522 21.237 0.036 0.212 0.000 -0.01 0.000 0.000 Benthic blue green 13.441 113.000 0.000 0.000 0.000 0.000 0.08 0.000 0.000 Benthic Algae 4.490 113.000 0.000 0.000 0.000 0.000 14.69 0.000 0.000 Planktonic blue green 190.856 85.000 0.000 15.918 0.212 0.000 213.74 0.000 0.000 Planktonic Algae 2.777 113.000 0.000 6.582 0.212 0.000 0.68 0.000 0.000 Macrophytes 0.259 7.727 0.000 0.000 0.000 0.000 0.30 0.000 0.000 Detritus 15577.456 0.000 0.000 0.000 0.000 0.000 0.000 0.000 2010 Common carp 2.875 0.385 3.487 0.000 0.000 0.000 0.00 0.883 0.000 Black bass 0.001 1.551 3.590 0.000 0.000 0.000 0.00 0.000 0.000 Black bullhead (1+) 0.023 1.099 3.551 0.000 0.000 0.000 0.00 0.000 0.000 Black bullhead (0) 0.001 1.000 3.589 0.000 0.000 0.000 0.00 0.000 0.000 Bluegill (1+) 0.008 1.403 3.565 0.000 0.000 0.000 0.00 0.000 0.000

Group B P/B Q/B Import Export Stocking BA Ycom Yrec Bluegill (0) 0.014 0.834 3.558 0.000 0.000 0.000 0.00 0.000 0.000 Channel catfish 0.260 0.617 3.519 0.000 0.000 0.030 0.00 0.000 0.040 Other benthivores 0.035 0.953 3.546 0.000 0.000 0.000 0.00 0.000 0.000 Crappie 0.069 0.859 3.537 0.000 0.000 0.000 0.00 0.000 0.013 Flathead catfish 0.230 0.557 3.521 0.000 0.000 0.000 0.00 0.000 0.000 Darters 0.001 2.322 3.592 0.000 0.000 0.000 0.00 0.000 0.000 Esocids 0.084 0.612 3.534 0.000 0.000 0.005 0.00 0.000 0.000 Walleye (1+) 0.531 0.591 3.509 0.000 0.000 0.000 0.00 0.000 0.081 Walleye (0) 0.033 0.934 3.547 0.000 0.000 0.371 0.00 0.000 0.000 White bass 0.087 0.798 3.534 0.000 0.000 0.000 0.00 0.000 0.004 Bigmouth buffalo 0.095 0.595 3.532 0.000 0.000 0.000 0.00 0.361 0.000 Min. and shiners 0.010 1.632 3.563 0.000 0.000 0.000 0.00 0.000 0.000 Sunfish 0.000 2.507 3.611 0.000 0.000 0.000 0.00 0.000 0.004

Yellow perch 0.044 1.196 3.543 0.000 0.000 0.000 0.00 0.000 0.001 219 Yellow bass (0) 0.631 1.038 3.507 0.000 0.000 0.000 0.00 0.000 0.000 Yellow bass (1+) 0.719 0.689 3.505 0.000 0.000 0.000 0.00 0.000 0.627 Worms 1.618 1.604 5.348 0.000 0.000 0.000 0.00 0.000 0.000 Chironomidae 2.278 1.463 4.876 0.000 0.000 0.000 0.00 0.000 0.000 Amphipods 0.063 3.853 12.844 0.000 0.000 0.000 0.00 0.000 0.000 Ben. Crust. 0.437 2.284 7.613 0.000 0.000 0.000 0.00 0.000 0.000 Benthic insects 0.105 3.355 11.184 0.000 0.000 0.000 0.00 0.000 0.000 Snails 0.050 4.096 13.654 0.000 0.000 0.000 0.00 0.000 0.000 Zebra mussels 40.409 0.673 2.243 0.000 0.000 0.000 0.00 0.000 0.000 Other bivalves 0.135 3.135 10.451 0.000 0.000 0.000 0.00 0.000 0.000 Copepod 0.143 3.242 12.468 0.419 0.212 0.000 0.00 0.000 0.000 Cladaceran 0.214 2.908 11.185 0.132 0.212 0.000 0.00 0.000 0.000 Rotifer 0.013 6.184 23.786 0.233 0.212 0.000 0.00 0.000 0.000 Benthic blue green 13.525 113.000 0.000 0.000 0.000 0.000 0.00 0.000 0.000 Benthic Algae 19.181 113.000 0.000 0.000 0.000 0.000 0.00 0.000 0.000 Planktonic blue green 404.599 85.000 0.000 55.756 0.212 0.000 0.00 0.000 0.000 Planktonic Algae 3.454 113.000 0.000 4.371 0.212 0.000 0.00 0.000 0.000

Group B P/B Q/B Import Export Stocking BA Ycom Yrec Macrophytes 0.259 7.727 0.000 0.000 0.000 0.000 0.00 0.000 0.000 Detritus 17432.334 0.000 0.000 0.000 0.000 0.000 0.000 0.000 220 221

Table 3. Sources of diet compositions used to parameterize annual ECOPATH models. Group Diet items Common carp Marsden (1997), English (1951), Rehder (1959), Effendie (1968), Bulkley et al. (1976), Inmon (1974) Black bass Liao (2001), Pelham et al. (2001) Black bullhead (1+) Forney (1952), Baur (1969) Black bullhead (0) Forney (1952), Baur (1969) Bluegill (1+) Shoup et al. (2007) Bluegill (0) Scott and Crossman (1998) Channel catfish Shoup et al. (2007) Other benthivores Assumed to be detritus and a small fraction of chironomids Crappie Liao (2001), Pelham (2001), Bulkley et al. (1976), Inmon (1974) Flathead catfish Pine et al. (2007), Weller and Robbins (1999) Darters Scott and Crossman (1998) Esocids Liao (2001) Walleye (1+) Liao (2001), Liao et al. (2002), Pelham et al. (2001) Mackie and Schloesser (1996) Walleye (0) Jernejcic (1969), Spykerman (1973), Inmon (1974), Bulkley et al. (1976) White bass Pelham et al. (2001), Jernejcic (1969), Bulkley et al. (1976), Inmon (1974) Bigmouth buffalo Sampson et al. (2009), Mccomish (1967) Min. and shiners Bulkley et al. (1976), Inmon (1974), Hartman et al. (1992) Sunfish Scott and Crossman (1998) Yellow perch Liao (2001), Pelham et al. (2001) Bulkley et al. (1976), Inmon (1974) Yellow bass (0) Kraus (1963), Bulkley et al. (1976), Inmon (1974) Yellow bass (1+) Kraus (1963), Bulkley et al. (1976), Inmon (1974) Worms Thorp and Covich (2001), Pennak (1989) Chironomidae Thorp and Covich (2001) Amphipods Thorp and Covich (2001) Ben. Crust. Thorp and Covich (2001) Benthic insects Thorp and Covich (2001) Snails Thorp and Covich (2001) Zebra mussels Mackie and Schloesser (1996) Other bivalves Thorp and Covich (2001) Copepods Thorp and Covich (2001) Cladcerans Thorp and Covich (2001) Rotifers Thorp and Covich (2001)

Table 4. Diet compositions used in mass balance of the 2007 ECOPATH model. Superscripts denote data pedigree index. Diet composition Predator Prey 2007 2008 2009 2010 Common carp Chironomidae 0.2501 0.0199 0.0205 0.0205 Benthic crustaceans (Amphipods) 0.0013 0.0014 0.0014 0.0014 Benthic crustaceans (non amphipod) 0.0110 0.0050 0.0052 0.0052 Snails 0.0031 0.0005 0.0005 0.0005 Zebra mussels 0.0011 0.0012 0.0012 0.0012 Carnivorous zooplankton 0.0011 0.0012 0.0012 0.0012 Benthic Algae 0.0011 0.0012 0.0012 0.0012 Planktonic blue green 0.0011 0.0012 0.0012 0.0012 Macrophytes 0.0011 0.0399 0.0154 0.0154 Detritus 0.7290 0.9285 0.9522 0.9522

Black bass Black bass 0.0046 0.0046 0.0046 0.0046 222 Bluegill (0) 0.0001 0.0001 0.0001 0.0001 Crappie 0.0327 0.0327 0.0327 0.0327 Darters 0.0376 0.0376 0.0376 0.0376 Walleye (0) 0.0065 0.0065 0.0065 0.0065 Yellow perch 0.0093 0.0093 0.0093 0.0093 Yellow bass (0) 0.2753 0.2753 0.2753 0.2753 Yellow bass (1+) 0.2754 0.2754 0.2754 0.2754 Worms 0.0094 0.0094 0.0094 0.0094 Chironomidae 0.0694 0.0694 0.0694 0.0694 Benthic crustaceans (Amphipods) 0.0866 0.0866 0.0866 0.0866 Benthic insects 0.0563 0.0563 0.0563 0.0563 Carnivorous zooplankton 0.0379 0.0379 0.0379 0.0379 Herbivorous zooplankton 0.0379 0.0379 0.0379 0.0379 Rotifer 0.0379 0.0379 0.0379 0.0379 Import 0.0231 0.0231 0.0231 0.0231

Diet composition Predator Prey 2007 2008 2009 2010 Black bullhead (1+) Minnows and shiners 0.0050 0.0020 0.0020 0.0020 Yellow perch 0.0010 0.0010 0.0010 0.0010 Worms 0.0009 0.0009 0.0009 0.0009 Chironomidae 0.7302 0.7324 0.7324 0.7324 Benthic crustaceans (Amphipods) 0.0010 0.0010 0.0010 0.0010 Benthic insects 0.0199 0.0200 0.0200 0.0200 Snails 0.0081 0.0081 0.0081 0.0081 Macrophytes 0.1299 0.1303 0.1303 0.1303 Detritus 0.0899 0.0901 0.0901 0.0901 Import 0.0141 0.0141 0.0141 0.0141 Black bullhead (0) Worms 0.0022 0.0022 0.0022 0.0022

Chironomidae 0.7545 0.7545 0.7545 0.7545 223 Benthic crustaceans (Amphipods) 0.0500 0.0500 0.0500 0.0500 Benthic insects 0.0560 0.0560 0.0560 0.0560 Import 0.1370 0.1370 0.1370 0.1370 Bluegill (1+) Chironomidae 0.1899 0.1899 0.1899 0.1899 Benthic insects 0.7306 0.7306 0.7306 0.7306 Snails 0.0194 0.0194 0.0194 0.0194 Zebra mussels 0.0113 0.0113 0.0113 0.0113 Other bivalves 0.0487 0.0487 0.0487 0.0487 Bluegill (0) Worms 0.0081 0.0081 0.0081 0.0081 Chironomidae 0.0203 0.0203 0.0203 0.0203 Benthic crustaceans (Amphipods) 0.0202 0.0202 0.0202 0.0202 Benthic insects 0.1220 0.1220 0.1220 0.1220 Carnivorous zooplankton 0.2765 0.2765 0.2765 0.2765 Herbivorous zooplankton 0.2765 0.2765 0.2765 0.2765 Rotifer 0.2765 0.2765 0.2765 0.2765

Diet composition Predator Prey 2007 2008 2009 2010 Channel catfish Darters 0.0000 0.0000 0.0000 0.0000 Minnows and shiners 0.0159 0.0051 0.0051 0.0051 Sunfish 0.0002 0.0002 0.0002 0.0002 Yellow perch 0.0016 0.0017 0.0017 0.0017 Yellow bass (0) 0.0256 0.0259 0.0259 0.0259 Yellow bass (1+) 0.3226 0.3260 0.3260 0.3260 Chironomidae 0.3477 0.3515 0.3515 0.3515 Benthic crustaceans (Amphipods) 0.0051 0.0052 0.0052 0.0052 Benthic insects 0.0082 0.0083 0.0083 0.0083 Snails 0.0200 0.0202 0.0202 0.0202 Zebra mussels 0.0171 0.0173 0.0173 0.0173

Other bivalves 0.0402 0.0406 0.0406 0.0406 224 Detritus 0.1960 0.1981 0.1981 0.1981 Other benthivores Chironomidae 0.3315 0.3315 0.3315 0.3315 Benthic crustaceans (Amphipods) 0.0051 0.0051 0.0051 0.0051 Benthic insects 0.0548 0.0548 0.0548 0.0548 Snails 0.1062 0.1062 0.1062 0.1062 Zebra mussels 0.0110 0.0110 0.0110 0.0110 Other bivalves 0.1068 0.1068 0.1068 0.1068 Benthic Algae 0.1640 0.1640 0.1640 0.1640 Detritus 0.2205 0.2205 0.2205 0.2205 Crappie Black bullhead (0) 0.0023 0.0023 0.0023 0.0023 Bluegill (0) 0.0001 0.0001 0.0001 0.0001 Darters 0.0078 0.0078 0.0078 0.0078 Minnows and shiners 0.0010 0.0010 0.0010 0.0010 Yellow bass (0) 0.0388 0.0388 0.0388 0.0388 Worms 0.0054 0.0054 0.0054 0.0054

Diet composition Predator Prey 2007 2008 2009 2010 Chironomidae 0.0980 0.0980 0.0980 0.0980 Benthic insects 0.4243 0.4243 0.4243 0.4243 Carnivorous zooplankton 0.1839 0.1839 0.1839 0.1839 Herbivorous zooplankton 0.1839 0.1839 0.1839 0.1839 Rotifer 0.0420 0.0420 0.0420 0.0420 Import 0.0120 0.0120 0.0120 0.0120 Flathead catfish Black bullhead (1+) 0.0040 0.0034 0.0035 0.0035 Black bullhead (0) 0.0114 0.0064 0.0067 0.0067 Bluegill (1+) 0.0258 0.0056 0.0059 0.0059 Bluegill (0) 0.0003 0.0006 0.0006 0.0006 Other benthivores 0.0010 0.0010 0.0011 0.0011

Crappie 0.0509 0.0039 0.0041 0.0041 225 Darters 0.0005 0.0011 0.0012 0.0012 Walleye (1+) 0.0762 0.0227 0.0238 0.0238 Walleye (0) 0.0206 0.0189 0.0094 0.0094 White bass 0.0240 0.0225 0.0236 0.0236 Minnows and shiners 0.0367 0.0023 0.0024 0.0024 Sunfish 0.0003 0.0002 0.0002 0.0002 Yellow perch 0.0254 0.0014 0.0014 0.0014 Yellow bass (0) 0.0302 0.0655 0.0311 0.0311 Yellow bass (1+) 0.4959 0.3433 0.3597 0.3597 Benthic crustaceans (Amphipods) 0.0120 0.0111 0.0117 0.0117 Import 0.1850 0.4902 0.5136 0.5136 Darters Worms 0.3995 0.3995 0.3995 0.3995 Chironomidae 0.4007 0.4007 0.4007 0.4007 Benthic crustaceans (Amphipods) 0.0995 0.0995 0.0995 0.0995 Benthic insects 0.1003 0.1003 0.1003 0.1003

Diet composition Predator Prey 2007 2008 2009 2010 Esocids Black bullhead (0) 0.0112 0.0112 0.0112 0.0112 Bluegill (1+) 0.0011 0.0011 0.0011 0.0011 Bluegill (0) 0.0000 0.0000 0.0000 0.0000 Channel catfish 0.0118 0.0118 0.0118 0.0118 Flathead catfish 0.0011 0.0011 0.0011 0.0011 Walleye (1+) 0.2766 0.2766 0.2766 0.2766 Walleye (0) 0.0101 0.0101 0.0101 0.0101 White bass 0.0011 0.0011 0.0011 0.0011 Bigmouth buffalo 0.0109 0.0109 0.0109 0.0109 Sunfish 0.0011 0.0011 0.0011 0.0011 Yellow perch 0.0056 0.0056 0.0056 0.0056

Yellow bass (0) 0.2000 0.0109 0.0109 0.0109 226 Yellow bass (1+) 0.2480 0.4374 0.4374 0.4374 Import 0.2210 0.2210 0.2210 0.2210 Walleye (1+) Black bullhead (0) 0.0010 0.0012 0.0012 0.0012 Bluegill (1+) 0.0104 0.0054 0.0054 0.0054 Bluegill (0) 0.0011 0.0014 0.0014 0.0014 Channel catfish 0.0000 0.0000 0.0000 0.0000 Other benthivores 0.0000 0.0000 0.0000 0.0000 Darters 0.0020 0.0005 0.0005 0.0005 Walleye (1+) 0.0000 0.0000 0.0000 0.0000 Walleye (0) 0.0037 0.0045 0.0045 0.0045 White bass 0.0026 0.0032 0.0032 0.0032 Bigmouth buffalo 0.0170 0.0210 0.0210 0.0210 Minnows and shiners 0.0018 0.0006 0.0006 0.0006 Sunfish 0.0001 0.0001 0.0001 0.0001 Yellow perch 0.0002 0.0002 0.0002 0.0002

Diet composition Predator Prey 2007 2008 2009 2010 Yellow bass (0) 0.0000 0.0000 0.0000 0.0000 Yellow bass (1+) 0.0002 0.0003 0.0003 0.0003 Worms 0.0186 0.0229 0.0229 0.0229 Chironomidae 0.2896 0.3574 0.3574 0.3574 Benthic crustaceans (non amphipod) 0.5151 0.4150 0.4150 0.4150 Benthic insects 0.0204 0.0251 0.0251 0.0251 Import 0.1160 0.1409 0.1409 0.1409 Walleye (0) Black bullhead (0) 0.0004 0.0004 0.0004 0.0004 Bluegill (0) 0.0000 0.0000 0.0000 0.0000 Crappie 0.0012 0.0012 0.0012 0.0012 Darters 0.0000 0.0000 0.0000 0.0000

Walleye (0) 0.0012 0.0012 0.0012 0.0012 227 Minnows and shiners 0.0008 0.0008 0.0008 0.0008 Yellow perch 0.0000 0.0000 0.0000 0.0000 Yellow bass (0) 0.0039 0.0039 0.0039 0.0039 Yellow bass (1+) 0.0782 0.0782 0.0782 0.0782 Chironomidae 0.1031 0.1031 0.1031 0.1031 Benthic insects 0.0004 0.0004 0.0004 0.0004 Snails 0.0003 0.0003 0.0003 0.0003 Carnivorous zooplankton 0.2012 0.2012 0.2012 0.2012 Herbivorous zooplankton 0.2012 0.2012 0.2012 0.2012 Rotifer 0.0080 0.0080 0.0080 0.0080 Import 0.4000 0.4000 0.4000 0.4000 White bass Yellow bass (0) 0.0071 0.0071 0.0071 0.0071 Yellow bass (1+) 0.1617 0.1617 0.1617 0.1617 Worms 0.0361 0.0361 0.0361 0.0361 Chironomidae 0.0342 0.0342 0.0342 0.0342

Diet composition Predator Prey 2007 2008 2009 2010 Benthic crustaceans (Amphipods) 0.0051 0.0051 0.0051 0.0051 Benthic insects 0.0053 0.0053 0.0053 0.0053 Carnivorous zooplankton 0.3684 0.3684 0.3684 0.3684 Herbivorous zooplankton 0.3684 0.3684 0.3684 0.3684 Rotifer 0.0139 0.0139 0.0139 0.0139 Bigmouth buffalo Worms 0.0997 0.0997 0.1513 0.1513 Chironomidae 0.7180 0.7180 0.7180 0.7180 Carnivorous zooplankton 0.0898 0.0898 0.0400 0.0400 Herbivorous zooplankton 0.0898 0.0898 0.0898 0.0898 Rotifer 0.0028 0.0028 0.0010 0.0010 Minnows and shiners Chironomidae 0.0278 0.0278 0.0278 0.0278

Benthic crustaceans (non amphipod) 0.0442 0.0442 0.0442 0.0442 228 Carnivorous zooplankton 0.4145 0.4145 0.4145 0.4145 Herbivorous zooplankton 0.4145 0.4145 0.4145 0.4145 Rotifer 0.0989 0.0989 0.0989 0.0989 Sunfish Worms 0.0707 0.0707 0.0707 0.0707 Chironomidae 0.2830 0.2830 0.2830 0.2830 Benthic crustaceans (Amphipods) 0.1400 0.1400 0.1400 0.1400 Benthic insects 0.1412 0.1412 0.1412 0.1412 Snails 0.1368 0.1368 0.1368 0.1368 Carnivorous zooplankton 0.0758 0.0758 0.0758 0.0758 Herbivorous zooplankton 0.0758 0.0758 0.0758 0.0758 Rotifer 0.0758 0.0758 0.0758 0.0758 Import 0.0008 0.0008 0.0008 0.0008 Yellow perch Minnows and shiners 0.0258 0.0258 0.0258 0.0258 Yellow perch 0.0005 0.0005 0.0005 0.0005 Yellow bass (0) 0.0086 0.0086 0.0086 0.0086

Diet composition Predator Prey 2007 2008 2009 2010 Worms 0.0172 0.0172 0.0172 0.0172 Chironomidae 0.2756 0.2756 0.2756 0.2756 Benthic crustaceans (Amphipods) 0.2220 0.2220 0.2220 0.2220 Benthic insects 0.1124 0.1124 0.1124 0.1124 Snails 0.0285 0.0285 0.0285 0.0285 Carnivorous zooplankton 0.0706 0.0706 0.0706 0.0706 Herbivorous zooplankton 0.0706 0.0706 0.0706 0.0706 Rotifer 0.0706 0.0706 0.0706 0.0706 Macrophytes 0.0977 0.0977 0.0977 0.0977 Yellow bass (0) Chironomidae 0.0674 0.0674 0.0674 0.0674 Snails 0.0120 0.0120 0.0120 0.0120

Carnivorous zooplankton 0.2050 0.2050 0.2050 0.2050 229 Herbivorous zooplankton 0.7000 0.7000 0.7000 0.7000 Rotifer 0.0151 0.0151 0.0151 0.0151 Yellow bass (1+) Yellow bass (0) 0.0036 0.0038 0.0038 0.0038 Worms 0.1073 0.1130 0.1130 0.1130 Chironomidae 0.6740 0.7098 0.7098 0.7098 Benthic crustaceans (Amphipods) 0.0073 0.0077 0.0077 0.0077 Benthic insects 0.0073 0.0077 0.0077 0.0077 Carnivorous zooplankton 0.1000 0.0527 0.0527 0.0527 Herbivorous zooplankton 0.1000 0.1053 0.1053 0.1053 Worms Chironomidae 0.0591 0.0591 0.0591 0.0591 Benthic insects 0.0010 0.0010 0.0010 0.0010 Benthic blue green 0.0500 0.0500 0.0500 0.0500 Benthic Algae 0.6900 0.6900 0.6900 0.6900 Detritus 0.1999 0.1999 0.1999 0.1999 Chironomidae Chironomidae 0.0591 0.0591 0.0591 0.0591

Diet composition Predator Prey 2007 2008 2009 2010 Benthic blue green 0.2200 0.2200 0.2200 0.2200 Benthic Algae 0.6000 0.6000 0.6000 0.6000 Import 0.1209 0.1209 0.1209 0.1209 Benthic crustaceans (Amphipods) Benthic blue green 0.2710 0.2710 0.2710 0.2710 Benthic Algae 0.7294 0.7294 0.7294 0.7294 Benthic crustaceans (non amphipod) Benthic blue green 0.8023 0.8023 0.8023 0.8023 Benthic Algae 0.1977 0.1977 0.1977 0.1977 Benthic insects Benthic crustaceans (Amphipods) 0.0050 0.0050 0.0050 0.0050 Benthic insects 0.0050 0.0050 0.0050 0.0050 Benthic Algae 0.9400 0.9400 0.9400 0.9400 Detritus 0.0500 0.0500 0.0500 0.0500

Snails Benthic blue green 0.2500 0.2500 0.2500 0.2500 230 Benthic Algae 0.7500 0.7500 0.7500 0.7500 Dreisseniidae Planktonic blue green 0.2500 0.2023 0.2023 0.2023 Planktonic Algae 0.7500 0.7977 0.7977 0.7977 Other bivalves Planktonic blue green 0.2023 0.2023 0.2023 0.2023 Planktonic Algae 0.7977 0.7977 0.7977 0.7977 Carnivorous zooplankton Carnivorous zooplankton 0.0500 0.0500 0.0500 0.0500 Herbivorous zooplankton 0.0500 0.0500 0.0500 0.0500 Rotifer 0.0100 0.0100 0.0100 0.0100 Planktonic blue green 0.2900 0.2900 0.2900 0.2900 Planktonic Algae 0.6000 0.6000 0.6000 0.6000 Herbivorous zooplankton Benthic blue green 0.2222 0.2222 0.2222 0.2222 Benthic Algae 0.7780 0.7780 0.7780 0.7780 Planktonic blue green 0.2222 0.2222 0.2222 0.2222 Planktonic Algae 0.6670 0.6670 0.6670 0.6670 Detritus 0.1111 0.1111 0.1111 0.1111

Diet composition Predator Prey 2007 2008 2009 2010 Rotifer Benthic blue green 0.2501 0.0199 0.0205 0.0205 Benthic Algae 0.0013 0.0014 0.0014 0.0014 Planktonic blue green 0.0110 0.0050 0.0052 0.0052 Planktonic Algae 0.0031 0.0005 0.0005 0.0005 Detritus 0.0011 0.0012 0.0012 0.0012

231 232

LITERATURE CITED

Baur, R. J. 1969. Digestion rate of the Clear Lake black bullhead. Thesis (M.S.)--Iowa State

University, 1969.

Bulkley, R. V., V. L. Spykermann, and L. E. Inmon. 1976. Food of the Pelagic Young of

Walleyes and Five Cohabiting Fish Species in Clear Lake, Iowa. Transactions of the

American Fisheries Society 105:77-83.

Effendie, M. I. 1968. Growth and food habitats of carp, Cyprinus carpio L., in Clear Lake,

IA. Masters thesis. Iowa State University, Ames.

English, T. S. 1951. Growth studies of the carp, Cyprinus carpio Linnaeus, in Clear Lake,

Iowa. Master Thesis. Iowa State College, Ames, IA.

Forney, J. L. 1952. Life history of the black bullhead (Ameirus melas) of Clear Lake, Iowa.

Master. Iowa State University, Ames.

Hartman, K. J., B. Vondracek, D. L. Parrish, and K. M. Muth. 1992. Diets of Emerald and

Spottail Shiners and Potential Interactions with Other Western Lake Erie

Planktivorous Fishes. Journal of Great Lakes Research 18:43-50.

Inmon, L. E. 1974. Feeding interactions between pelagic larvae of walleye, Stizostedion

vitreum vitreum (Mitchell) and associated fish species in Clear Lake, Iowa. Master.

Iowa State University, Ames.

Jernejcic, F. A. 1969. Prey selectivity of Clear Lake walleye. Master. Iowa State University,

Ames.

Kraus, R. 1963. Food habits of the yellow bass, Roccus mississippiensis, Clear Lake, Iowa,

Summer 1962. Iowa Academy of Science 70:209-214. 233

Liao, H. 2001. Assessment of diet and consumption dynamics of the adult piscivorous fish

community in Spirit Lake, Iowa. Ph.D. Iowa State Univeristy, Ames, IA.

Liao, H., C. L. Pierce, and J. G. Larscheid. 2002. Diet dynamics of the adult piscivorous fish

community in Spirit Lake, Iowa, USA 1995-1997. Ecology of Freshwater Fish

11:178-189.

Mackie, G. L., and D. W. Schloesser. 1996. Comparative Biology of Zebra Mussels in

Europe and North America: An Overview. Amer. Zool. 36:244-258.

Marsden, J. E. 1997. Common carp diet includes zebra mussels and lake trout eggs. Journal

of Freshwater Ecology 12:491-492.

Mccomish, T. S. 1967. Food Habits of Bigmouth and Smallmouth Buffalo in Lewis and

Clark Lake and Missouri River. Transactions of the American Fisheries Society

96:70-&.

Pelham, M. E., C. L. Pierce, and J. G. Larscheid. 2001. Diet dynamics of the juvenile

piscivorous fish community in Spirit Lake, Iowa, USA, 1997-1998. Ecology of

Freshwater Fish 10:198-211.

Pennak, R. W. 1989. Freshwater invertebrates of the United States, Third edition. John Wiley

and Sons, Inc., New York.

Pine, W. E., T. J. Kwak, and J. A. Rice. 2007. Modeling management scenarios and the

effects of an introduced apex predator on a coastal riverine fish community.

Transactions of the American Fisheries Society 136:105–120.

Rehder, D. D. 1959. Some aspects of the life history of the carp, Cyprinus carpio, in the Des

Moines River, Boone County, Iowa. Thesis (M.S.)--Iowa State College, 1959. 234

Sampson, S. J., J. H. Chick, and M. A. Pegg. 2009. Diet overlap among two Asian carp and

three native fishes in backwater lakes on the Illinois and Mississippi rivers. Biological

Invasions 11:483-496.

Scott, W. B., and E. J. Crossman. 1998. Freshwater fishes of Canada. Galt House Oakville.

Shoup, D. E., S. P. Callahan, D. H. Wahl, and C. L. Pierce. 2007. Size-specific growth of

bluegill, largemouth bass and channel catfish in relation to prey availability and

limnological variables. Journal of Fish Biology 70:21-34.

Spykerman, V. L. 1973. Food habits, growth, and distribution of larval walleye, Stizostedion

vitreum vitreum (Mitchell), in Clear Lake, Iowa. Master. Iowa State University,

Ames.

Thorp, J. H., and A. P. Covich. 2001. Ecology and classification of North American

freshwater invertebrates, Second edition. Academic Press, Inc. , New York.

Weller, R. R., and C. Robbins. 1999. Food habits of flathead catfish in the Altamaha River

system, Georgia. Proceedings of the Fifty-Third Annual Conference of the

Southeastern Association of Fish and Wildlife Agencies:35-41.

235

APPENDIX 3. LAKE CHARACTERISTICS MODULE

Overview.—The lake characteristics module was developed to provide a method to easily modify the model to a similar system. In particular, variables in this module are lake- specific values characterizing lake morphology and initial baseline loading values for variables outside of the lake system boundaries (e.g., TSSVM,2010). The module performs relatively few calculations; however it does provide output given a particular value during simulations. For example, the proportion of lake susceptible to resuspension based on a given wind speed is evaluated using a graphical function. A graphical function essentially

“looks up” and returns a specific value given an input value. The module contains two graphical functions for the proportion of lake benthic area susceptible to resuspension and the lake hypsography. These graphical functions are used to calculate fraction of the lake susceptible to resuspension. This value is provided to the suspended solids module to limit in-lake resuspension.

Module equations

1. 1/, Lake flushing rate (1/year)

2. , Fraction of lake bottom susceptible to wave

resuspension (unitless)

2.1. •, Fraction of lake bottom susceptible to wave

resuspension as a graphical function of wind speed (unitless; Figure 1)

2.1.1. , fraction of lake bottom susceptible to wave resuspension

as a graphical function of wind speed (unitless; Figure 1)

2.1.2. , Lake area (m2; Table 1)

2 2.2. •, lake area occupied by macrophytes (m ) 236

, 0 2.2.1. , proportion of lake area 0, 0 occupied by macrophytes (unitless)

2.2.1.1. , macrophyte biomass, input from food web module (g/m2)

2.2.1.2. 2• proportion of lake area in the photic zone which is assumed to be 2 times water column transparency indexed by Secchi transparency (Scheffer 1998) (unitless; Figure 2)

LITERATURE CITED

Anthony, J. L., and J. A. Downing. 2003. Physical and chemical impacts of wind and boat

traffic on Clear Lake, Iowa, USA. Lake and Reservoir Management 19:1-14.

Downing, J. A., J. Kopaska, and D. Bonneau. 2001. Clear Lake diagnostic and feasibility

study. Iowa Department of Natural Resources, Des Moines.

IADNR TMDL & Water Quality Assessment Section. 2005. Total maximum daily load for

nutrients and algae, Clear Lake, Cerro Gordo County, Iowa

Scheffer, M. 1998. Ecology of shallow lakes, 1st edition. Chapman and Hall, New York.

237

Table 1. Lake characteristics module parameter values, units, description and sources. Parameter Value Units Description and source D 2.9 m Mean lake depth (Downing et al. 2001) V 42925168 m3 Lake volume (IADNR TMDL & Water Quality Assessment Section 2005) LR 4.3 yr Lake retention time (IADNR TMDL & Water Quality Assessment Section 2005) 3 Qvm 200030 m /yr Annual flow into Clear Lake from Ventura Marsh (IADNR TMDL & Water Quality Assessment Section 2005) 3 TSSVM,2010 70.69 g/m 2010 mean total suspended solids of Clear Lake inflow from Ventura Marsh, field collected A 14640000 m2 Lake area (IADNR TMDL & Water Quality Assessment Section 2005) TPloading 6411982 g Total phosphorous loading from external sources except Ventura Marsh (i.e., ground water, precipitation), this value is the total annual phosphorus loading minus the 24% of the annual phosphorus budget attributed to Ventura Marsh loading (Downing et al. 2001; IADNR TMDL & Water Quality Assessment Section 2005) 3 TPVM,2010 0.28 g/m Total phosphorus concentration of Clear Lake inflow from Ventura Marsh, field collected 2010 mean W 1 unitless Forcing function to simulate increases or decrease in mean wind speed, ranges from 0 to greater than 1. W2010 16.5 m/s Baseline wind speed mean of 2010 Graphical functions Graphical function that returns the fraction of lake area that would be considered photic (i.e., light reaching the benthos) as 2•Secchi Transparency (Figure 2). Graphical function that returns the fraction of lake area that would be considered susceptible to wave resuspension (Figure 1) Inputs from related modules Bmacrophyte Input from food web module Input from water quality module

238

1.0

0.8

0.6

0.4

0.2 Proportion of lake area susceptible to wind lake susceptibleto area of resuspension Proportion

0.0

0 20406080100

Wind speed (m/s)

Figure 1. Proportion of lake area susceptible to wind resuspension. Data modified from Anthony and Downing (2003). 239

1.0

0.8

0.6

0.4 Proportion of lake area lake of Proportion

0.2

0.0

0123456

Depth (m)

Figure 2. Graphical function relating the proportion of lake area at or less than a given depth.

240

APPENDIX 4: FOOD WEB MODULE

Overview.—The food web is a significant ecosystem component. This module uses a

modification of the time dynamic food web model ECOSIM (Walters et al. 1997; Walters et

al. 2000; Christensen and Walters 2004). The major modification to the existing ECOSIM

framework implemented in SLESM was the addition of nutrient and light limitations on primary production () assuming Michelis-Menton kinetics (Christensen and Walters

2004). Edible (i.e., greens, diatoms) and inedible (i.e., cyanobacteria) phytoplankton

biomass (hereafter phytoplankton collectively refers to edible and inedible pelagic

phytoplankton groups) was assumed to be limited by both nutrients and light during

simulations by multiplying growth rate limitations of both factors. This is a more neutral

assumption to assuming a single factor is limiting at any given time (i.e., Liebig’s law of the

minimum) (Chapra 1997; Scheffer 1998). For example, decreased Secchi transparency may

limit primary production in shallow lakes; however this limitation may be compensated by

increased TP concentrations. Alternatively, if both TP and light were limiting, primary

production would be reduced reflecting the effect of both limitations. This modification

allows more realistic limitation on primary production. Consumption and predation assume a

Lotka Volterra type donor-recipient system (i.e., bottom up control), however recent

advances in modeling trophic interactions has allowed for a mix of top down and bottom up

control to be modeled by assuming that trophic interactions occur in foraging arenas (Walters

and Christensen 2007). Foraging arenas add biological realism to simulated trophic dynamics by accounting for biomass that is unavailable for consumption (i.e., hiding). Thus, a predator’s effective search rate (aij) is modified to account for a mix of top down and

′ bottom up control by incorporating predation vulnerability. Vulnerability rates () are 241

extremely difficult, if not impossible to estimate in practice (Walters and Martell 2004;

Walters and Christensen 2007). A value of 2 is commonly assumed to reflect a mix of

′ bottom up and top down control of trophic dynamics. Increasing reflects bottom up control of trophic dynamics. See Walters and Martell (2004) for additional overview of predation modeling.

Food web primary producer and consumer biomass dynamics were simulated for food web component i by the ordinary differential equation (ODE):

where,

, 1. ,

• • • • 1.1. • •

1.1.1. biomass of food web group i (Table 1)

1.1.2. relative maximum , limits primary production (Table 1)

1.1.3. production to biomass ratio for group i (Table 1)

1.1.4. •, , saturation constant for light limitation such

that primary production returns to baseline values when ,

1.1.4.1. , defined in equation 1.1.2 242

1.1.4.2. ,, baseline Secchi transparency (Table 1)

1.1.5. , Secchi transparency (Table 1)

1.1.6. • saturation constant for P limitation such that

primary production returns to baseline values when

1.1.6.1. , defined in equation 1.1.2

1.1.6.2. , baseline total phosphorous concentration (Table 1)

1.1.7. , total phosphorous concentration (Table 1)

1.2. • ∑ total consumption by consumer group j on prey i

1.2.1. gross growth efficiency of consumer j (Table 1)

••• 1.2.2. ′ consumption of group i by predator j ••

1.2.2.1. defined in equation 1.1.1 (Table 1)

1.2.2.2. predator biomass (Table 1)

′ 1.2.2.3. , effective search rate for food web group ••

i by predator j

1.2.2.3.1. initial biomass of food web group i (Table 1)

1.2.2.3.2. initial biomass of predator j (Table 1)

1.2.2.3.3. initial consumption amount of food web group i by

predator j (Table 1)

′ 1.2.2.3.4. •2,,, vulnerability of food web group i to

predation by predator j

′ 1.2.2.3.4.1. , vulnerability rate (Table 1) 243

1.2.2.3.4.2. 2,,, initial predation mortality rate for food web

group i by predator j (Table 1)

′ 1.2.2.3.5. , defined in equation 1.2.2.3.4.1

2. stocking biomass of food web group i (Table 1)

3. • , net migration of food web group i

3.1. , net migration rate of food web

group i

3.1.1. , immigration rate of food web group i (Table 1)

3.1.2. , export rate of food web group i Table 1

3.2. , defined in equation 1.1.1

4. , harvest amount of food web group i (Table 1)

5. ∑ , total predation of food web group i

5.1. , consumption of prey i by predator j, defined in Equation 1.2.2

6. • , other mortality of food web group i

6.1. •1, other mortality rate of food web group i

6.1.1. , defined in equation 1.1.3

6.1.2. , ectrophic efficiency of food web group i (Table 1)

6.2. defined in equation 1.1.1

Multiple stanza groups.—Age 0 black bullhead, bluegill, walleye and yellow bass

numerically dominate the system and were represented in the baseline ECOPATH model as

two stanzas, a juvenile (0-12 months) and adult (> 12 months) group. This was done to 244

model the ontological diet shift among these dominant species. For example age 0 yellow bass are zooplanktivorous, while adults are insectivorous, feeding primarily on benthic insects (Kraus 1963; Atchison 1967; Inmon 1974). Additionally age 0 walleye are zooplanktivorous, while adults are piscivorous (Inmon 1974; Liao et al. 2002; Liao et al.

2004; Peterson et al. 2006). Modeling ‘split’ biomass pools (i.e., juvenile, adults) requires additional models to keep track of abundance and biomass graduated from the juvenile to adult stanza using a delay differential equation (Walters et al. 2000). Simply put, biomass flows occurring due to reproductive losses, juvenile recruitment, and biomass graduation are delayed by the length of the juvenile period. For example, the number of juveniles produced per unit biomass of adults takes 1 year to graduate into the adult stanza, and therefore this period is accounted for using a 1 year delay in recruitment. See Walters et al. (2000) for an overview the methods used to model split biomass pools used in this module. To ease interpretation of equations, a prefix for juveniles (juv) and adults (ad) was added to the food web component index i. Consumer groups represented by split biomass pools were simulated food web component i by the system of delay differential equations and symbols modified

from Walters et al. (2000):

245

, , , , ,

, ,

, , , , , , , , , ,

, , , , ,

where,

7. ,, Juvenile biomass (state variable)

7.1. , , •,, production of juvenile food web group i

7.1.1. , defined in equation 1.2.2

7.1.2. , defined in equation 1.2.1

7.2. , amount of biomass stocked (Table 1)

7.3. , , • ,, immigration amount of juvenile food

web group i

7.3.1. ,, defined in equation 3.1

7.3.2. , defined in equation 1.1.1

7.4. , , • ,, recruitment of

,, 7.4.1. , , amount of juvenile fish recruited per adult biomass ,,

,,•, 7.4.1.1. ,, , initial number of recruits of , 246

, 7.4.1.1.1. ,, ,,

7.4.1.1.1.1. ,, defined in equation 1.2

7.4.1.1.1.2. ,, weight at graduation from juvenile pool (Table

, 7.4.1.1.1.3. 0, •,, effective weight at

recruitment to juvenile pool

7.4.1.1.2. , defined in equation 1.1.3

7.4.1.2. ,,, initial biomass of adult food web group i

7.4.2. ,, defined in equation 1.1.1

7.5. , , • ,

7.5.1. ,, defined in equation 7.4.1.1.1.3

7.5.2. , •,, number of juveniles graduated

into adult stanza

7.5.2.1. ,, defined in equation 1.1.3

7.5.2.2. ,,, number of juveniles recruited to juvenile stanza

7.6. ,, defined in Equation 5

7.7. , defined in Equation 5

8. ,, Juvenile numbers (state variable)

,•, 8.1. , , number of juveniles recruited to juvenile pool ,

8.1.1. ,, defined in equation 7.4.1

8.1.2. ,, defined in equation 1.1.1 247

8.1.3. 0,, defined in equation 7.4.1.1.2

8.2. ,, number of juvenile fish stocked (Table 1)

8.3. , , ,

8.3.1. , defined in Equation 7.5.2

8.3.2. , , •,

,,, 8.3.2.1. , , mortality rate for juveniles of food ,

web group i

8.3.2.1.1. ,, other mortality rate for juveniles of food web group i

8.3.2.1.2. 2, , predation mortality rate for juveniles of ,

food web group i

8.3.2.1.3. , net migration rate of juvenile food web group i (Table

8.3.2.1.4. ,, defined in equation 1.1.1

8.3.2.2. , number of juveniles of group

9. ,, Adult biomass (state variable)

9.1. , defined in Equation 7.1

9.2. , defined in Equation 3

, 9.3. , • ,

9.3.1. , defined in equation 8.3.2.1

9.3.2. ,, defined in equation 7.7.2

9.4. ,, defined in equation 7.4 248

9.5. ,, harvest amount food web group (Table 1)

9.6. ,, same as predation, defined in equation 5

9.7. ,, same as other mortality, defined in equation 6

10. ,, Adult numbers (state variable)

10.1. , defined in Equation 8.3.1

10.2. , , •,

,,, 10.2.1. , same as defined in equation 8.3.2 ,

10.2.2. , number of adults of group i

11. Initial values for state variables

, 11.1. 0, number of juveniles ,,

11.1.1. , defined in Equation 1.2

11.1.2. , mean weight of an individual at graduation see Table 1

,•, 11.1.3. 0,

11.1.3.1. , defined in equation 1.1.3

,/, 11.2. 0, 0, •

, 11.2.1. 0, 0, •,/1 , number of initial recruits

11.2.1.1. 0, defined in Equation 11.1

11.2.1.2. , defined in equation 1.1.3

11.2.1.3. , defined in equation 1.1.3

249

LITERATURE CITED

Atchison, G. J. 1967. Contributions to the life history of the yellow bass, Roccus

mississippiensis (Jordan and Eigenmann), in Clear Lake, Iowa. Thesis (M.S.)--Iowa

State University, 1967.

Chapra, S. C. 1997. Surface water-quality modeling. New York : McGraw-Hill, New York.

Christensen, V., and C. J. Walters. 2004. Ecopath with Ecosim: methods, capabilities and

limitations. Ecological Modelling 172:109-139.

Inmon, L. E. 1974. Feeding interactions between pelagic larvae of walleye, Stizostedion

vitreum vitreum (Mitchell) and associated fish species in Clear Lake, Iowa. Master.

Iowa State University, Ames.

Kraus, R. 1963. Food habits of the yellow bass, Roccus mississippiensis, Clear Lake, Iowa,

Summer 1962. Iowa Academy of Science 70:209-214.

Liao, H., C. L. Pierce, and J. G. Larscheid. 2002. Diet dynamics of the adult piscivorous fish

community in Spirit Lake, Iowa, USA 1995-1997. Ecology of Freshwater Fish

11:178-189.

Liao, H., C. L. Pierce, and J. G. Larscheid. 2004. Consumption dynamics of the adult

piscivorous fish community in Spirit Lake, Iowa. North American Journal of

Fisheries Management 24:890-902.

Peterson, D. L., J. Peterson, and R. F. Carline. 2006. Effects of Zooplankton density on

survival of stocked walleye fry in five Pennsylvania reservoirs. Journal of Freshwater

Ecology 21:121-129.

Scheffer, M. 1998. Ecology of shallow lakes, 1st edition. Chapman and Hall, New York. 250

Walters, C., and V. Christensen. 2007. Adding realism to foraging arena predictions of

trophic flow rates in Ecosim ecosystem models: Shared foraging arenas and bout

feeding. Ecological Modelling 209:342-350.

Walters, C., V. Christensen, and D. Pauly. 1997. Structuring dynamic models of exploited

ecosystems from trophic mass-balance assessments. Reviews in Fish Biology and

Fisheries 7:139-172.

Walters, C., D. Pauly, V. Christensen, and J. F. Kitchell. 2000. Representing density

dependent consequences of life history strategies in aquatic ecosystems: EcoSim II.

Ecosystems 3:70-83.

Walters, C. J., and S. J. D. Martell. 2004. Fisheries ecology and management. Princeton

University Press, Princeton, NJ.

251

Table 1. Food web module parameter values, units, description and sources. Parameter Value Units Description and source 2 Table 2 g/m Biomass of group i Table 2 Unitless the maximum relative PB -1 Table 2 1•year initial PB rate from baseline ECOPATH model Table 2 Unitless gross growth efficiency, production to consumption ratio from baseline ECOPATH model - 2 g/m Biomass of predator j, Same as -2 Table 2 g•m initial biomass of group I from baseline ECOPATH model -2 Table 2 g•m initial biomass of predator j from baseline ECOPATH model -2 Table 3 g•m initial consumption of predator j on prey i from baseline ECOPATH model ′ 2 Unitless vulnerability rate, assumed to be 2 for all groups to reflect a mix of top down and bottom up control -1 2,, Table 3 1•year initial predation mortality rate of predator j on prey i from baseline ECOPATH model -2 Table 4 g•m amount of biomass stocked for group i from baseline ECOPATH model -1 Table 2 1•year net migration rate of group i from baseline ECOPATH model

Table 2 Unitless ecotrophic efficiency of group i from baseline ECOPATH model Multiple stanza items: juvenile biomass -2 , Table 4 g•m Biomass of fish stocked , Table 4 # Number of juvenile fish stocked , Table 4 g Mean weight of juvenile fish stocked , Table 4 g Weight of fish at graduation, calculated from field data Inputs from related modules Input from fishery module Input from water quality module , Input from water quality module Input from nutrients module 252

Parameter Value Units Description and source Input from nutrients module

253

Table 2. Food web group indices and parameter values. Group i j B0 PB r Common carp 1 1 16.900 0.385 - 0.136 0 Black bass 2 2 0.001 1.551 - 0.011 0 Black bullhead (1+) 3 3 0.200 1.099 - 0.007 0 Black bullhead (0) 4 4 0.015 1.700 - 0.343 0 Bluegill (1+) 5 5 0.008 1.403 - 0.835 0 Bluegill (0) 6 6 0.001 2.100 - 0.880 0 Channel catfish 7 7 0.260 0.617 - 0.274 0 Other benthivores 8 8 0.069 0.916 - 0.008 0 Crappie 9 9 0.069 0.859 - 0.257 0 Flathead catfish 10 10 0.150 0.557 - 0.004 0 Darters 11 11 0.002 2.322 - 0.955 0 Esocids 12 12 0.084 0.612 - 0.000 0 Walleye (1+) 13 13 0.400 0.591 - 0.687 0 Walleye (0) 14 14 0.008 11.300 - 0.142 0 White bass 15 15 0.087 0.798 - 0.278 0 Bigmouth buffalo 16 16 1.300 0.595 - 0.506 0 Minnows and shiners 17 17 0.010 1.632 - 0.783 0 Sunfish 18 18 0.002 2.800 - 0.865 0 Yellow perch 19 19 0.044 1.196 - 0.111 0 Yellow bass (0) 20 20 0.104 1.750 - 0.381 0 Yellow bass (1+) 21 21 1.000 1.300 - 0.984 0 Worms 22 22 1.657 1.916 - 0.359 0 Chironomidae 23 23 2.291 5.200 - 0.965 0 Benthic crustaceans 24 24 0.063 5.700 - 0 (Amphipods) 0.469 Benthic crustaceans (non 25 25 0.437 3.853 - 0 amphipod) 0.497 Benthic insects 26 26 0.124 3.355 - 0.630 0 Snails 27 27 0.055 4.096 - 0.397 0 Dreisseniidae 28 28 40.784 0.673 - 0.003 0 Other bivalves 29 29 0.135 3.135 - 0.153 0 Copepod 30 30 0.250 4.640 - 0.761 -7.4x10-9 Cladaceran 31 31 0.280 5.100 - 0.963 -6.4 x10-9 Rotifer 32 32 0.013 7.500 - 0.757 -4.3 x10-7 Benthic blue green 33 - 13.525 113.000 2 0.009 0 Benthic Algae 34 - 19.181 113.000 3 0.016 0 Planktonic blue green 35 - 404.599 85.000 2 0.001 -7.2 x10-11 Planktonic Algae 36 - 3.454 113.000 3 0.207 -1.1 x10-9 Macrophytes 37 - 0.259 9.000 2 0.435 0 Detritus 38 - 17432.330 - - - -

254

Table 3. Values for Qij0 and M2ij0. Values are from the baseline ECOPATH model. Predator Prey M2ij Qij Common carp Chironomidae 0.52615 1.205508 Benthic crustaceans (Amphipods) 1.305738 0.082318 Benthic crustaceans (non amphipod) 0.695635 0.30428 Snails 0.546139 0.030168 Dreisseniidae 0.001724 0.070316 Copepod 0.282774 0.070694 Benthic Algae 0.003773 0.072373 Planktonic blue green 0.000179 0.072373 Macrophytes 3.496507 0.906532 Detritus 0 56.11642 Black bass Black bass 0.016501 2.17E-05 Bluegill (0) 0.000389 4.44E-07 Crappie 0.002223 0.000154 Darters 0.118264 0.000177 Walleye (0) 0.003751 3.08E-05 Yellow perch 0.000992 4.39E-05 Yellow bass (0) 0.012472 0.001298 Yellow bass (1+) 0.001298 0.001298 Worms 2.66E-05 4.41E-05 Chironomidae 0.000143 0.000327 Benthic crustaceans (Amphipods) 0.006473 0.000408 Benthic insects 0.002146 0.000266 Copepod 0.000714 0.000179 Cladaceran 0.000638 0.000179 Rotifer 0.01364 0.000179 Black bullhead (1+) Minnows and shiners 0.147848 0.001425 Yellow perch 0.016726 0.000741 Worms 0.000404 0.000669 Chironomidae 0.227047 0.520207 Benthic crustaceans (Amphipods) 0.011371 0.000717 Benthic insects 0.114591 0.014177 Snails 0.104677 0.005782 Macrophytes 0.356848 0.092519 Detritus 0 0.064015 Black bullhead (0) Worms 5.46E-05 9.06E-05 Chironomidae 0.013664 0.031307 Benthic crustaceans (Amphipods) 0.032909 0.002075 Benthic insects 0.018767 0.002322 Bluegill (1+) Chironomidae 0.002503 0.005736 Benthic insects 0.178341 0.022064 255

Predator Prey M2ij Qij Snails 0.0106 0.000586 Dreisseniidae 8.4E-06 0.000343 Other bivalves 0.010936 0.001472 Bluegill (0) Worms 1.95E-05 3.23E-05 Chironomidae 3.54E-05 8.1E-05 Benthic crustaceans (Amphipods) 0.001275 8.04E-05 Benthic insects 0.003933 0.000487 Copepod 0.004412 0.001103 Cladaceran 0.00394 0.001103 Rotifer 0.084266 0.001103 Channel catfish Darters 0.01109 1.66E-05 Minnows and shiners 0.47913 0.004618 Sunfish 0.07513 0.00015 Yellow perch 0.034157 0.001512 Yellow bass (0) 0.227317 0.023651 Yellow bass (1+) 0.297896 0.297896 Chironomidae 0.140159 0.32113 Benthic crustaceans (Amphipods) 0.074797 0.004715 Benthic insects 0.061581 0.007619 Snails 0.334379 0.018471 Dreisseniidae 0.000387 0.015767 Other bivalves 0.275661 0.037098 Detritus 0 0.181012 Other benthivores Chironomidae 0.035092 0.080402 Benthic crustaceans (Amphipods) 0.019538 0.001232 Benthic insects 0.107492 0.013299 Snails 0.466412 0.025764 Dreisseniidae 6.56E-05 0.002674 Other bivalves 0.192472 0.025902 Benthic Algae 0.002074 0.039776 Detritus 0 0.05347 Crappie Black bullhead (0) 0.039176 0.000574 Bluegill (0) 0.030187 3.44E-05 Darters 1.273402 0.00191 Minnows and shiners 0.025405 0.000245 Yellow bass (0) 0.091322 0.009501 Worms 0.000802 0.001329 Chironomidae 0.01047 0.023989 Benthic insects 0.839687 0.103885 Copepod 0.180073 0.045018 256

Predator Prey M2ij Qij Cladaceran 0.16078 0.045018 Rotifer 0.785094 0.010277 Flathead catfish Black bullhead (1+) 0.007958 0.001592 Black bullhead (0) 0.20497 0.003002 Bluegill (1+) 0.312658 0.002649 Bluegill (0) 0.230642 0.000263 Other benthivores 0.006904 0.000473 Crappie 0.026357 0.001825 Darters 0.348908 0.000523 Walleye (1+) 0.026809 0.010723 Walleye (0) 0.516321 0.004244 White bass 0.122521 0.010625 Minnows and shiners 0.112454 0.001084 Sunfish 0.054189 0.000108 Yellow perch 0.014687 0.00065 Yellow bass (0) 0.134616 0.014006 Yellow bass (1+) 0.161871 0.161871 Benthic crustaceans (Amphipods) 0.08319 0.005245 Darters Worms 0.001299 0.002153 Chironomidae 0.000942 0.002159 Benthic crustaceans (Amphipods) 0.008499 0.000536 Benthic insects 0.004368 0.00054 Esocids Black bullhead (0) 0.226542 0.003318 Bluegill (1+) 0.03924 0.000332 Bluegill (0) 0.002916 3.32E-06 Channel catfish 0.013396 0.003478 Flathead catfish 0.00216 0.000324 Walleye (1+) 0.204404 0.081762 Walleye (0) 0.363538 0.002988 White bass 0.003758 0.000326 Bigmouth buffalo 0.002477 0.00322 Sunfish 0.162981 0.000326 Yellow perch 0.037064 0.001641 Yellow bass (0) 0.031065 0.003232 Yellow bass (1+) 0.129309 0.129309 Walleye (1+) Black bullhead (0) 0.108018 0.001582 Bluegill (1+) 0.819571 0.006943 Bluegill (0) 1.583433 0.001805 Channel catfish 0.000126 3.26E-05 Other benthivores 0.000481 3.3E-05 257

Predator Prey M2ij Qij Darters 0.463593 0.000695 Walleye (1+) 8.8E-05 3.52E-05 Walleye (0) 0.702028 0.00577 White bass 0.046753 0.004055 Bigmouth buffalo 0.020713 0.026927 Minnows and shiners 0.081943 0.00079 Sunfish 0.087964 0.000176 Yellow perch 0.006534 0.000289 Yellow bass (0) 3.08E-06 3.2E-07 Yellow bass (1+) 0.000327 0.000327 Worms 0.017711 0.029351 Chironomidae 0.199668 0.457477 Benthic crustaceans (non amphipod) 1.214422 0.531204 Benthic insects 0.260055 0.032173 Walleye (0) Black bullhead (0) 0.004243 6.21E-05 Bluegill (0) 0.000546 6.23E-07 Crappie 0.002667 0.000185 Darters 0.000829 1.24E-06 Walleye (0) 0.022675 0.000186 Minnows and shiners 0.01238 0.000119 Yellow perch 0.000139 6.14E-06 Yellow bass (0) 0.005818 0.000605 Yellow bass (1+) 0.012109 0.012109 Chironomidae 0.006966 0.015961 Benthic insects 0.000501 6.19E-05 Snails 0.000978 5.4E-05 Copepod 0.124582 0.031146 Cladaceran 0.111234 0.031146 Rotifer 0.094035 0.001231 White bass Yellow bass (0) 0.020869 0.002171 Yellow bass (1+) 0.049552 0.049552 Worms 0.006675 0.011061 Chironomidae 0.004578 0.01049 Benthic crustaceans (Amphipods) 0.024558 0.001548 Benthic insects 0.013059 0.001616 Copepod 0.451542 0.112886 Cladaceran 0.403162 0.112886 Rotifer 0.32433 0.004246 Bigmouth buffalo Worms 0.419196 0.694674 Chironomidae 1.43904 3.297107 258

Predator Prey M2ij Qij Copepod 0.734757 0.183689 Cladaceran 1.472026 0.412167 Rotifer 0.350809 0.004592 Minnows and shiners Chironomidae 0.000417 0.000955 Benthic crustaceans (non amphipod) 0.003473 0.001519 Copepod 0.056946 0.014236 Cladaceran 0.050844 0.014236 Rotifer 0.259367 0.003395 Sunfish Worms 0.000308 0.000511 Chironomidae 0.000892 0.002043 Benthic crustaceans (Amphipods) 0.016039 0.001011 Benthic insects 0.008243 0.00102 Snails 0.017883 0.000988 Copepod 0.00219 0.000547 Cladaceran 0.001955 0.000547 Rotifer 0.041816 0.000547 Yellow perch Minnows and shiners 0.419159 0.00404 Yellow perch 0.001885 8.34E-05 Yellow bass (0) 0.012942 0.001347 Worms 0.001625 0.002693 Chironomidae 0.018866 0.043225 Benthic crustaceans (Amphipods) 0.55238 0.034824 Benthic insects 0.142488 0.017628 Snails 0.08085 0.004466 Copepod 0.044303 0.011076 Cladaceran 0.039556 0.011076 Rotifer 0.84609 0.011076 Macrophytes 0.059099 0.015322 Yellow bass (0) Chironomidae 0.008929 0.020459 Snails 0.06609 0.003651 Copepod 0.248835 0.062209 Cladaceran 0.758642 0.21242 Rotifer 0.349629 0.004577 Yellow bass (1+) Yellow bass (0) 0.129499 0.013473 Worms 0.239036 0.396121 Chironomidae 1.085993 2.488212 Benthic crustaceans (Amphipods) 0.427433 0.026947 Benthic insects 0.217808 0.026947 Copepod 0.738342 0.184586 Cladaceran 1.318468 0.369171 259

Predator Prey M2ij Qij Worms Chironomidae 0.272959 0.625399 Benthic insects 0.085779 0.010612 Benthic blue green 0.039117 0.529069 Benthic Algae 0.380654 7.301147 Detritus 0 2.115145 Chironomidae Chironomidae 1.024466 2.347241 Benthic blue green 0.645986 8.737055 Benthic Algae 1.242318 23.82833 Benthic crustaceans Benthic blue green 0.024 0.32461 (Amphipods) Benthic Algae 0.045549 0.873646 Benthic crustaceans Benthic blue green 0.333261 4.5074 (non amphipod) Benthic Algae 0.057908 1.110707 Benthic insects Benthic crustaceans (Amphipods) 0.109139 0.006881 Benthic insects 0.056089 0.006939 Benthic Algae 0.067811 1.300659 Detritus 0 0.069178 Snails Benthic blue green 0.013941 0.188556 Benthic Algae 0.029492 0.565669 Dreisseniidae Planktonic blue green 0.045746 18.50874 Planktonic Algae 21.12855 72.97435 Other bivalves Planktonic blue green 0.00081 0.327879 Planktonic Algae 0.374289 1.292729 Copepod Copepod 0.662857 0.165714 Cladaceran 0.591837 0.165714 Rotifer 2.531847 0.033143 Planktonic blue green 0.002376 0.961143 Planktonic Algae 0.575759 1.988572 Cladaceran Planktonic blue green 0.002907 1.176084 Planktonic Algae 1.192147 4.11747 Rotifer Planktonic blue green 0.000227 0.09185 Planktonic Algae 0.079821 0.275688 Detritus 0 0.045925

260

Table 4. Stocking data and weight at graduation for fish groups. Food web group , , , , Black bullhead (0) 0 0 - 16 Bluegill (0) 0 0 - 0.5 Yellow bass (0) 0 0 - 5.9 Walleye (0) 18082256 0.32 0.371 120 Esocids 10132 7.62 0.005 -

261

APPENDIX 5. FISHERY MODULE

Overview.—Fishery yield dynamics were modeled using the following equation:

, ,

where,

1. , , •

1.1. , is the fishing mortality of fishery k on group i (Table 1)

1.2. is the biomass of group i from food web module (Table 1)

Table 1. Fishery module parameter values, units, description and sources. Parameter Value Units Description and source Input from food web module -1 , Table 2 year fishing mortality of fishery k on group i; estimated in 2010 ECOPATH model

Table 2. Fishing mortality rates (year-1). Fishing mortality estimates are from the 2010 ECOPATH model. Only non-zero fishing mortality rates are reported Group i Fishery k F Common carp 1 Commercial 1 0.052 Bigmouth buffalo 16 0.278 Black bullhead (1+) 3 Recreational 2 0.001 Channel catfish 7 0.155 Crappie 9 0.189 Walleye (1+) 13 0.175 White bass 15 0.049 Sunfish 18 2.041 Yellow perch 19 0.021 Yellow bass (1+) 21 0.627

262

APPENDIX 6. NUTRIENTS MODULE

Overview.—Water column phosphorous (P) can be quantified in several forms

including soluble, insoluble, and bound to sediment (Scheffer 1998). While there is

discrepancy among what form of phosphorous is directly utilized by primary producers

(Scheffer 1998) it is well established that Clear Lake, phosphorous is the nutrient limiting

phytoplankton production (Downing et al. 2001b). Soluble reactive phosphorous (SRP) is

thought to be the form of phosphorous directly utilized by primary producers; however SRP

in lake ecosystems is typically below detection limits, due to almost instantaneous uptake by

phytoplankton communities. Therefore, total phosphorous is typically used to predict

phytoplankton biomass (e.g., Watson et al. 1992; Downing et al. 2001c; Kalff 2003).

Therefore, P is used to limit phytoplankton primary production in CLESM. Total phosphorous concentration (TP) at any given time was modeled as sum of phosphorus components in the following equation:

where,

• • 1. , P input from Ventura Marsh (g• m-3•year-1)

-3 1.1. , TP concentration of Ventura Marsh flows (g• m )(Table 1)

3 -1 1.2. , annual flow to Clear Lake from Ventura Marsh (m •year ) (Table 1)

1.3. , forcing function of time, used to simulate reductions in TP concentration of

Ventura Marsh (unitless) (Table 1)

1.4. , Clear Lake volume (m3) (Table 1) 263

∑ -3 2. , , amount of P excreted by food web consumers (g•m )

-3 2.1. , •,, amount of P excreted by food web group i (g•m )

2.1.1. , P assimilation efficiency for food web group i (unitless) (Table 1)

- 2.1.2. , • ∑ ,, P consumed by food web group i (g•m

-2 2.1.2.1. , amount prey component i consumed by predator j (g•m ) (Table

2.1.2.2. , fraction of biomass food web group i that is P (unitless)

(Table 1)

2.1.3. mean lake depth, converts from g•m-2 to g•m-3

3. 3.975 • 55.4, predictive relationship relating turbity to

-3 developed by Anthony et al. (2001) (g•m )

3.1. , water column turbity (NTU) (Table 1)

• • 4. , P contained in

phytoplankton (g•m-3)

4.1. , fraction of edible phytoplankton biomass that is P (unitless) (Table

4.2. , fraction of inedible phytoplankton biomass that is P (unitless)

(Table 1)

-2 4.3. , edible phytoplankton biomass (g•m ) (Table 1)

-2 4.4. , inedible phytoplankton biomass (g•m ) (Table 1)

4.5. , mean lake depth (m) (Table 1) 264

• 5. , P loading to Clear Lake from external sources (e.g., ground water)

(g•m-3)

5.1. , amount of P loading (g•m-3) (Table 1)

5.2. , fraction of external loading attributed to Ventura Marsh

(g•m-3) (Table 1)

5.3. , lake volume (m3) (Table 1)

LITERATURE CITED

Anthony, J., J. M. Farre, and J. Downing. 2001. Physical limnology of Clear Lake. Iowa

Department of Natural Resources, Des Moines.

Downing, J. A., J. Kopaska, and D. Bonneau. 2001a. Clear Lake diagnostic and feasibility

study. Iowa Department of Natural Resources, Des Moines.

Downing, J. A., J. Kopaska, R. Cordes, and N. Eckles. 2001b. Limnology of Clear Lake.

Iowa Department of Natural Resources, Des Moines.

Downing, J. A., S. B. Watson, and E. McCauley. 2001c. Predicting cyanobacteria dominance

in lakes. Canadian Journal of Fisheries and Aquatic Sciences 58:1905-1908.

IADNR TMDL & Water Quality Assessment Section. 2005. Total maximum daily load for

nutrients and algae, Clear Lake, Cerro Gordo County, Iowa

Kalff, J. 2003. Limnology, 2nd edition. Prentice Hall, Upper Saddle River, NJ.

Scheffer, M. 1998. Ecology of shallow lakes, 1st edition. Chapman and Hall, New York.

Verant, M. L., M. L. Konsti, K. D. Zimmer, and C. A. Deans. 2007. Factors influencing

nitrogen and phosphorus excretion rates of fish in a shallow lake. Freshwater Biology

52:1968-1981. 265

Watson, S., E. McCauley, and J. A. Downing. 1992. Sigmoid relationships between

phosphorus, algal biomass, and algal community structure Canadian Journal of

Fisheries and Aquatic Sciences 49:2605-2610.

Zimmer, K. D., B. R. Herwig, and L. M. Laurich. 2006. Nutrient excretion by fish in wetland

ecosystems and its potential to support algal production. Limnology and

Oceanography 51:197-207.

Table 1. Nutrients module parameter values, units, description and sources. Parameter Value(s) Units Description and source 1 unitless forcing function to simulate successful reduction of external Ventura Marsh loading values can range from 0 to greater than 1 See Table 2 unitless Assimilation efficiency of ingested phosphorous (Zimmer et al. 2006; Verant et al. 2007) 3 See Table 2 g/g/m Fraction of biomass that is phosphorous (Zimmer et al. 2006; Verant et al. 2007) 3 200030 m /yr Annual inflow from Ventura Marsh (IADNR TMDL & Water Quality Assessment Section 2005) 8436818 g g P loaded from external sources (i.e., groundwater, precipitation) (Downing et al. 2001a; IADNR TMDL & Water Quality Assessment Section 2005) 0.24 unitless Proportion of total P loading attributed to Ventura Marsh loading (Downing et al. 2001a) Inputs from related modules Lake volume, input from lake characteristics module Total phosphorus concentration, input from lake characteristics module Mean lake depth, input from lake characteristics module Consumption of prey i by predator j, input from food web module Turbidity, input from water quality module Biomass of pelagic phytoplankton, input from food web module

266

Table 2. Food web group phosphorous fractions () and phosphorous assimilation efficiencies () all values are from (Zimmer et al. 2006; Verant et al. 2007). Group Common carp 0.005 0.75 Black bass 0.005 0.75 Black bullhead (1+) 0.005 0.75 Black bullhead (0) 0.005 0.75 Bluegill (1+) 0.005 0.75 Bluegill (0) 0.005 0.75 Channel catfish 0.005 0.75 Other benthivores 0.005 0.75 Crappie 0.005 0.75 Flathead catfish 0.005 0.75 Darters 0.005 0.75 Esocids 0.005 0.75 Walleye (1+) 0.005 0.75 Walleye (0) 0.005 0.75 White bass 0.005 0.75 Bigmouth buffalo 0.004 0.75 Minnows and shiners 0.005 0.75 Sunfish 0.005 0.75 Yellow perch 0.005 0.75 Yellow bass (0) 0.005 0.75 Yellow bass (1+) 0.005 0.75 Worms 0.0011 0.75 Chironomidae 0.0011 0.75 Benthic crustaceans (Amphipods) 0.00011 0.75 Benthic crustaceans (non amphipod) 0.00011 0.75 Benthic insects 0.0004 0.75 Snails 0.0011 0.75 Dreisseniidae 0.0018 0.75 Other bivalves 0.00072 0.75 Copepod 0.0017 0.75 Cladaceran 0.00072 0.75 Rotifer 0.0017 0.75 Benthic blue green 0.001 - Benthic Algae 0.001 - Planktonic blue green 0.0008 - Planktonic Algae 0.0008 - Macrophytes 0.001 - Detritus 0.001 -

267

APPENDIX 7. SUSPENDED SEDIMENT MODULE

Overview.—Total suspended sediment (Ssupended) concentration is a function of resuspension of benthic solids, external loading, settling of solids, and flushing (Scheffer

1998). Suspended sediment dynamics were simulated using the system of ODEs:

where,

• • 2.

2.1. 1 1, effect of a change in common carp biomass ,

relative to 2010 biomass value,

2.1.1. amplitude of effect of a relative change of common carp biomass on

resuspension rate

2 2.1.2. , common carp biomass (g/m )

2 2.1.3. ,, initial common carp biomass (g/m ) (Table 1)

2.2. 1 1, amplitude of effect of a change in average wind

speed relative to mean 2010 windspeed

2.2.1. , effect of a relative change of common carp biomass on resuspension

rate

2.2.2. , windspeed (m/s)

2.2.3. average windspeed (m/s) (Table 1) 268

• 3. , amount of sediment input to Clear Lake from Ventura Marsh.

3 3.1. , total suspended sediment concentration of Ventura Marsh inflow (g/m ) (Table

3 3.2. , flow into Clear Lake from Ventura Marsh (m /year) (Table 1)

3.3. , mean lake depth (m) (Table 1)

4. • amount of suspended sediment loss to settling (g/m3/year)

4.1. , settling rate (m/year) (Table 1)

4.2. , mean lake depth (m) (Table 1)

3 4.3. , state variable, suspended sediment concentration (g/m )

5. • , amount of suspended sediment lost to lake outflow

(g/m3/year)

5.1. , lake flushing rate (1/year)

5.1.1. , lake retention rate (year) (Table 1)

3 5.2. , state variable, suspended sediment concentration (g/m )

Parameterization

The ODE used to simulate suspended solids dynamics was parameterized by mass balance.

The system of ODEs:

Γ .• • 0.22 • 10000 •15 •15 . . . Γ (1) •15 • 0.22 • 10000 • 10000 . .

269

were set to equal 0 and the resuspension rate Γ was constrained to be greater than 0, S was constrained to be +/- 20% of 16.79, and B was constrained to be greater than or equal to zero.

Assuming the burial process has a negligible impact on benthic sediment dynamics will be 0.

This assumption is based on median seston particle size of 16-66µm (silt sized)available from the (Iowa Lakes Information System 2005). The parameters were solved by

minimizing the deviations of and from 0 (i.e., steady state). Values from mass

balance optimization can be found in Table 1.

LITERATURE CITED

Iowa Lakes Information System. 2005. Iowa Lakes Information System.

http://limnology.eeob.iastate.edu/lakereport/.

Kalff, J. 2003. Limnology, 2nd edition. Prentice Hall, Upper Saddle River, NJ.

Larscheid, J. G. 2005. Population densities, biomass and age-growth of common carp and

black bullheads in Clear Lake and Ventura Marsh. Iowa Department of Natural

Resources Report F-160-R, Des Moines.

Scheffer, M. 1998. Ecology of shallow lakes, 1st edition. Chapman and Hall, New York.

270

Table 1. Nutrients module parameter values, units, description and sources. Parameter Value Units Description and source Γ 0.14 g/m3/yr Resuspension rate found by mass balance. S 15 g/m3 Total suspended solids concentration, mean of field collected data for 2010 3 Svm 70.69 g/m Total suspended solids concentration of Ventura Marsh inflow, mean of field collected data for 2010. s 17.93 m/yr Suspended particle settling rate. Assumed based on a median suspended particle size of 18-66μm (Iowa Lakes Information System 2005) which is 0.05 m/d (Kalff 2003), solved by mass balance allowing to vary +/- 20% from 16.79 B 0 g/m3/year Burial rate, mass balance result was 0, so assumed to be 0 (i.e., negligible over an annual period). 0.925 Unitless Tuned to so that simulated TSS concentrations was approximately 47 mg/L when carp biomass reached 500 kg/ha (Iowa Lakes Information System 2005; Larscheid 2005). 1 Unitless Assumed Inputs from related modules D Mean lake depth, input from lake characteristics module V Lake volume, input from lake characteristics module LR Lake retention time, input from lake characteristics module Qvm Annual inflow from Ventura Marsh, input from lake characteristics module Bcarp Common carp biomass, input from food web module Bcarp,2010 Baseline common carp biomass value from 2010 ECOPATH model, input from food web module Macrophyte coverage, input from lake characteristics module , Baseline macrophyte coverage, input from food web module W Wind speed, input from lake characteristics module W2010 Baseline wind speed, input from lake characteristics module

271

APPENDIX 8. WATER QUALITY MODULE

Overview.—The water quality module calculates common water quality metrics based

on inputs from the food web, nutrients and suspended solids modules. Water quality metrics are Secchi transparency and turbidity. Turbidity is a function of suspended solids and phytoplankton in the water column (Appendix 10). Secchi transparency is calculated based on the results of Appendix 11. Cholorphyll a is a commonly measure index of phytoplankton biomass, however a good predictive relationship between phytoplankton biomass and chlorophyll a could not be found and the model explicitly models phytoplankton biomass,

and therefore this index was not included. Turbidity (Nephelometric Turbidity Units, NTU)

was estimated as ..• (Appendix 9). Total suspended solids

.• .• (TSS) was estimated as . Secchi

transparency was estimated as .•.• .•

(Appendix 10).

Table 1. Nutrients module parameter values, units, description and sources. Parameter Inputs from related modules Total phosphorus concentration, input from lake characteristics module Suspended sediment concentration, input from suspended sediment module Mean lake depth, input from lake characteristics module Biomass of edible phytoplankton, input from food web module Biomass of inedible phytoplankton, input from food web module

272

APPENDIX 9. PREDICTING TURBIDITY FROM TOTAL SUSPENDED SOLIDS

Overview.—A predictive relationship of turbidity (T, NTU) and total phosphorous

(TP, mg/L) was developed by (Anthony et al. 2001), where TP was predicted as:

55.4+3.975•T. This equation is used to predict the amount of phosphorous in the water column as phosphorous bound to sediment. A predictive relationship of total suspended solids (TSS, mg/L) and turbity was developed to utilize this equation in CLESM.

Analysis.—Clear Lake specific values of T and TSS were used to develop the predictive relationship from IADNR ambient lakes monitoring program, Iowa Lakes

Information System, and this study. T and TSS were loge transformed and a linear model used to predict T from TSS (Figure 1). The best fit linear model was:

2 Loge(T)=-0.235+0.954•loge(TSS) (R =0.57).

LITERATURE CITED

Anthony, J., J. M. Farre, and J. Downing. 2001. Physical limnology of Clear Lake. Iowa

Department of Natural Resources, Des Moines.

273

3 Log Turbidity (NTU) 2

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Log Total Suspended Solids (mg/L)

Figure 1. Relationship of total suspended solids and turbidity. The solid line is represents the best fit line.

274

APPENDIX 10. PREDICTING SECCHI TRANSPARENCY

Overview.—Secchi transparency is an integrated measure of light penetration in aquatic systems. Presence of solids in the water column limits light penetration and Secchi transparency is a function of organic and inorganic components in the water column.

Components in the water column include phytoplankton and solids in varying constituent forms (i.e., organic, inorganic).

Analysis.—Clear Lake specific total suspended solids (TSS; mg/L), edible and inedible phytoplankton biomass concentration (mg/L) values were used to develop the predictive relationship from IADNR ambient lakes monitoring program, Iowa Lakes

Information System, and this study. The relationship of Secchi transparency and water column materials typically hyperbolic in shape (Figure 1) (Scheffer 1998). Therefore the model was fit by linear regression to the inverse of Zsd. The best fit model was:

. .•TSS.•B.•B

LITERATURE CITED

Scheffer, M. 1998. Ecology of shallow lakes, 1st edition. Chapman and Hall, New York.

275

APPENDIX 11. SIMULATION OUTPUT

This appendix contains simulation graphical output for all state variables evaluated using the scenarios in Table 1.

Table 1. Scenarios evaluated using CLESM. Increased ecotrophic efficiency (EE) allows biomass to grow by decreasing the other mortality rate (Mo) since Mo=PB•(1-EE) and decreasing EE increases Mo simulating biomass declines. Baseline values reflect baseline estimates from the 2010 ECOPATH model. Scenario EEcommon carp EEzebra mussels Fcommon carp LoadingVentura Marsh 1 Baseline Baseline Baseline Baseline 2 Baseline Baseline Baseline 0.7•Baseline 3 Baseline Baseline 1.3•Baseline Baseline 4 Baseline Baseline 1.3•Baseline 0.7•Baseline 5 Baseline 10•Baseline Baseline Baseline 6 Baseline 10•Baseline Baseline 0.7•Baseline 7 Baseline 10•Baseline 1.3•Baseline Baseline 8 Baseline 10•Baseline 1.3•Baseline 0.7•Baseline 9 Baseline 0.01•Baseline Baseline Baseline 10 Baseline 0.01•Baseline Baseline 0.7•Baseline 11 Baseline 0.01•Baseline 1.3•Baseline Baseline 12 Baseline 0.01•Baseline 1.3•Baseline 0.7•Baseline 13 5•Baseline Baseline Baseline Baseline 14 5•Baseline Baseline Baseline 0.7•Baseline 15 5•Baseline Baseline 1.3•Baseline Baseline 16 5•Baseline Baseline 1.3•Baseline 0.7•Baseline 17 5•Baseline 10•Baseline Baseline Baseline 18 5•Baseline 10•Baseline Baseline 0.7•Baseline 19 5•Baseline 10•Baseline 1.3•Baseline Baseline 20 5•Baseline 10•Baseline 1.3•Baseline 0.7•Baseline 21 5•Baseline 0.01•Baseline Baseline Baseline 22 5•Baseline 0.01•Baseline Baseline 0.7•Baseline 23 5•Baseline 0.01•Baseline 1.3•Baseline Baseline 24 5•Baseline 0.01•Baseline 1.3•Baseline 0.7•Baseline

276

Scenario 1

Common carp Black bullhead (0) Bluegill (0) Crappie 1.3 Black bass Bluegill (1+) 1.3 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 1.2 1.2

1.1 1.1

1.0 1.0

0.9 0.9

0.8 0.8

Darters Walleye (0) Bigmouth buffalo Yellow perch 1.3 Esocids White bass 1.3 Minnows and shiners Yellow bass (0) Walleye (1+) Sunfish 1.2 1.2

1.1 1.1

1.0 1.0

0.9 0.9

0.8 0.8

Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.3 Worms Benthic crustacea 1.3 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.2 1.2

1.1 1.1

1.0 1.0

0.9 0.9

0.8 0.8

Cladaceran Benthic Algae Planktonic Algae Macrophytes 1.3 Rotifer Planktonic blue green 1.3 Benthic blue green 1.2 1.2

1.1 1.1

1.0 1.0

0.9 0.9

0.8 0.8

0 1020304050 0 1020304050 Years

Figure 1. Simulated biomass dynamics for scenario 1 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

277

Scenario 2

Common carp Black bullhead (0) Bluegill (0) Crappie 1.3 Black bass Bluegill (1+) 1.3 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 1.2 1.2

1.1 1.1

1.0 1.0

0.9 0.9

0.8 0.8

Darters Walleye (0) Bigmouth buffalo Yellow perch 1.3 Esocids White bass 1.3 Minnows and shiners Yellow bass (0) Walleye (1+) Sunfish 1.2 1.2

1.1 1.1

1.0 1.0

0.9 0.9

0.8 0.8

Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.3 Worms Benthic crustacea 1.3 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.2 1.2

1.1 1.1

1.0 1.0

0.9 0.9

0.8 0.8

Cladaceran Benthic Algae Planktonic Algae Macrophytes 1.3 Rotifer Planktonic blue green 1.3 Benthic blue green 1.2 1.2

1.1 1.1

1.0 1.0

0.9 0.9

0.8 0.8

0 1020304050 0 1020304050 Years

Figure 2. Simulated biomass dynamics for scenario 2 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

278

Scenario 3

Common carp Black bullhead (0) Bluegill (0) Crappie 1.6 Black bass Bluegill (1+) 1.3 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 1.4 1.2

1.2 1.1

1.0 1.0

0.8 0.9

0.6 0.8

Darters Walleye (0) 6 Bigmouth buffalo Yellow perch 1.3 Esocids White bass Minnows and shiners Yellow bass (0) Walleye (1+) Sunfish 1.2 5 4 1.1 3 1.0 2 0.9 1 0.8

Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves Worms Benthic crustacea 1.3 Snails Copepod 1.2 Chironomidae Zebra mussels 1.2 Relative biomass

1.1 1.0 1.0

0.8 0.9

0.8 0.6

Cladaceran Benthic Algae 50 Planktonic Algae Macrophytes 1.3 Rotifer Planktonic blue green Benthic blue green 1.2 40

1.1 30

1.0 20

0.9 10

0.8 0

0 1020304050 0 1020304050 Years

Figure 3. Simulated biomass dynamics for scenario 3 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

279

Scenario 4

Common carp Black bullhead (0) Bluegill (0) Crappie 1.6 Black bass Bluegill (1+) 1.3 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 1.4 1.2

1.2 1.1

1.0 1.0

0.8 0.9

0.6 0.8

Darters Walleye (0) 6 Bigmouth buffalo Yellow perch 1.3 Esocids White bass Minnows and shiners Yellow bass (0) Walleye (1+) Sunfish 1.2 5 4 1.1 3 1.0 2 0.9 1 0.8

Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves Worms Benthic crustacea 1.3 Snails Copepod 1.2 Chironomidae Zebra mussels 1.2 Relative biomass

1.1 1.0 1.0

0.8 0.9

0.8 0.6

Cladaceran Benthic Algae 50 Planktonic Algae Macrophytes 1.3 Rotifer Planktonic blue green Benthic blue green 1.2 40

1.1 30

1.0 20

0.9 10

0.8 0

0 1020304050 0 1020304050 Years

Figure 4. Simulated biomass dynamics for scenario 4 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

280

Scenario 5 2.0 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) Channel catfish Flathead catfish 2.0 Black bullhead (1+) Other benthivores 1.5

1.5 1.0

1.0 0.5

2.0 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass Minnows and shiners Yellow bass (0) Walleye (1+) 1.5 Sunfish 1.5 1.0 1.0

0.5 0.5

0.0

Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves Worms Benthic crustacea 2.0 Snails Copepod Chironomidae Zebra mussels 2.0 Relative biomass 1.5

1.5 1.0

0.5 1.0 0.0

Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.5 Benthic blue green 3

1.0 2

0.5 1

0.0 0

0 1020304050 0 1020304050 Years

Figure 5. Simulated biomass dynamics for scenario 5 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

281

Scenario 6 2.0 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) Channel catfish Flathead catfish 2.0 Black bullhead (1+) Other benthivores 1.5

1.5 1.0

1.0 0.5

2.0 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass Minnows and shiners Yellow bass (0) Walleye (1+) 1.5 Sunfish 1.5 1.0 1.0

0.5 0.5

0.0

Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves Worms Benthic crustacea 2.0 Snails Copepod Chironomidae Zebra mussels 2.0 Relative biomass 1.5

1.5 1.0

0.5 1.0 0.0

Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.5 Benthic blue green 3

1.0 2

0.5 1

0.0 0

0 1020304050 0 1020304050 Years

Figure 6. Simulated biomass dynamics for scenario 6 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

282

Scenario 7

Common carp Black bullhead (0) 2.0 Bluegill (0) Crappie Black bass Bluegill (1+) Channel catfish Flathead catfish 2.0 Black bullhead (1+) Other benthivores 1.5

1.5 1.0

0.5 1.0

0.0

2.5 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 6 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 5 1.5 4 3 1.0 2 0.5 1 0

Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 2.5 Worms Benthic crustacea 2.0 Snails Copepod Chironomidae Zebra mussels Relative biomass 2.0 1.5

1.0 1.5 0.5 1.0 0.0

2.0 60 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green Benthic blue green 50 1.5 40

1.0 30

20 0.5 10

0.0 0

0 1020304050 0 1020304050 Years

Figure 7. Simulated biomass dynamics for scenario 7 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

283

Scenario 8

Common carp Black bullhead (0) 2.0 Bluegill (0) Crappie Black bass Bluegill (1+) Channel catfish Flathead catfish 2.0 Black bullhead (1+) Other benthivores 1.5

1.5 1.0

0.5 1.0

0.0

2.5 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 6 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 5 1.5 4 3 1.0 2 0.5 1 0

Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 2.5 Worms Benthic crustacea 2.0 Snails Copepod Chironomidae Zebra mussels Relative biomass 2.0 1.5

1.0 1.5 0.5 1.0 0.0

2.0 60 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green Benthic blue green 50 1.5 40

1.0 30

20 0.5 10

0.0 0

0 1020304050 0 1020304050 Years

Figure 8. Simulated biomass dynamics for scenario 8 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

284

Scenario 9 2.0 Common carp Black bullhead (0) Bluegill (0) Crappie 1.3 Black bass Bluegill (1+) Channel catfish Flathead catfish Black bullhead (1+) 1.8 Other benthivores 1.2 1.6

1.1 1.4

1.0 1.2

0.9 1.0

0.8 0.8

Darters Walleye (0) Bigmouth buffalo Yellow perch 1.8 Esocids White bass 2.0 Minnows and shiners Yellow bass (0) Walleye (1+) Sunfish 1.6 1.8 1.4 1.6

1.2 1.4 1.2 1.0 1.0 0.8 0.8

Yellow bass (1+) Benthic crustacea 2.2 Benthic insects Other bivalves 1.3 Worms Benthic crustacea 2.0 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.2 1.8

1.1 1.6 1.4 1.0 1.2 0.9 1.0 0.8 0.8

Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.6 1.6 Benthic blue green 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8

0 1020304050 0 1020304050 Years

Figure 9. Simulated biomass dynamics for scenario 9 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

285

Scenario 10 2.0 Common carp Black bullhead (0) Bluegill (0) Crappie 1.3 Black bass Bluegill (1+) Channel catfish Flathead catfish Black bullhead (1+) 1.8 Other benthivores 1.2 1.6

1.1 1.4

1.0 1.2

0.9 1.0

0.8 0.8

Darters Walleye (0) Bigmouth buffalo Yellow perch 1.8 Esocids White bass 2.0 Minnows and shiners Yellow bass (0) Walleye (1+) Sunfish 1.6 1.8 1.4 1.6

1.2 1.4 1.2 1.0 1.0 0.8 0.8

Yellow bass (1+) Benthic crustacea 2.2 Benthic insects Other bivalves 1.3 Worms Benthic crustacea 2.0 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.2 1.8

1.1 1.6 1.4 1.0 1.2 0.9 1.0 0.8 0.8

Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.6 1.6 Benthic blue green 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8

0 1020304050 0 1020304050 Years

Figure 10. Simulated biomass dynamics for scenario 10 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

286

Scenario 11

Common carp Black bullhead (0) 1.8 Bluegill (0) Crappie 1.6 Black bass Bluegill (1+) Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 1.4 1.6

1.2 1.4

1.0 1.2

0.8 1.0

0.6 0.8

Darters Walleye (0) 6 Bigmouth buffalo Yellow perch 1.8 Esocids White bass Minnows and shiners Yellow bass (0) Walleye (1+) Sunfish 5 1.6 4 1.4 3 1.2 2 1.0 1 0.8

Yellow bass (1+) Benthic crustacea 2.0 Benthic insects Other bivalves Worms Benthic crustacea Snails Copepod 1.2 Chironomidae 1.8 Zebra mussels Relative biomass 1.6 1.0 1.4 1.2 0.8 1.0 0.8 0.6

Cladaceran Benthic Algae 50 Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.6 Benthic blue green 40 1.4 30 1.2 20

1.0 10

0.8 0

0 1020304050 0 1020304050 Years

Figure 11. Simulated biomass dynamics for scenario 11 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

287

Scenario 12

Common carp Black bullhead (0) 1.8 Bluegill (0) Crappie 1.6 Black bass Bluegill (1+) Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 1.4 1.6

1.2 1.4

1.0 1.2

0.8 1.0

0.6 0.8

Darters Walleye (0) 6 Bigmouth buffalo Yellow perch 1.8 Esocids White bass Minnows and shiners Yellow bass (0) Walleye (1+) Sunfish 5 1.6 4 1.4 3 1.2 2 1.0 1 0.8

Yellow bass (1+) Benthic crustacea 2.0 Benthic insects Other bivalves Worms Benthic crustacea Snails Copepod 1.2 Chironomidae 1.8 Zebra mussels Relative biomass 1.6 1.0 1.4 1.2 0.8 1.0 0.8 0.6

Cladaceran Benthic Algae 50 Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.6 Benthic blue green 40 1.4 30 1.2 20

1.0 10

0.8 0

0 1020304050 0 1020304050 Years

Figure 12. Simulated biomass dynamics for scenario 12 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

288

Scenario 13 8 1.4 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 6 1.0 0.8 4 0.6 0.4 2 0.2

0 0.0

1.4 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 2.0 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 Benthic blue green 1.5 1.0 0.8 1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 13. Simulated biomass dynamics for scenario 13 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

289

Scenario 14 8 1.4 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 6 1.0 0.8 4 0.6 0.4 2 0.2

0 0.0

1.4 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 2.0 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 Benthic blue green 1.5 1.0 0.8 1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 14. Simulated biomass dynamics for scenario 14 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

290

Scenario 15 1.4 6 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish 5 Black bullhead (1+) Other benthivores 1.0 4 0.8 3 0.6 2 0.4

1 0.2 0.0

1.4 2.5 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8 0.6 1.0 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 2.0 Benthic blue green 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 15. Simulated biomass dynamics for scenario 15 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

291

Scenario 16 1.4 6 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish 5 Black bullhead (1+) Other benthivores 1.0 4 0.8 3 0.6 2 0.4

1 0.2 0.0

1.4 2.5 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8 0.6 1.0 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 2.0 Benthic blue green 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 16. Simulated biomass dynamics for scenario 16 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

292

Scenario 17 8 1.4 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 6 1.0 0.8 4 0.6 0.4 2 0.2

0 0.0

1.4 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

2.0 1.4 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 Benthic blue green 1.5 1.0 0.8 1.0 0.6

0.5 0.4 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 17. Simulated biomass dynamics for scenario 17 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

293

Scenario 18 8 1.4 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 6 1.0 0.8 4 0.6 0.4 2 0.2

0 0.0

1.4 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

2.0 1.4 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 Benthic blue green 1.5 1.0 0.8 1.0 0.6

0.5 0.4 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 18. Simulated biomass dynamics for scenario 18 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

294

Scenario 19 1.4 6 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish 5 Black bullhead (1+) Other benthivores 1.0 4 0.8 3 0.6 2 0.4

1 0.2 0.0

1.4 2.5 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8 0.6 1.0 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 2.0 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 Benthic blue green 1.0 1.5 0.8 1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 19. Simulated biomass dynamics for scenario 19 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

295

Scenario 20 1.4 6 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish 5 Black bullhead (1+) Other benthivores 1.0 4 0.8 3 0.6 2 0.4

1 0.2 0.0

1.4 2.5 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8 0.6 1.0 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 2.0 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 Benthic blue green 1.0 1.5 0.8 1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 20. Simulated biomass dynamics for scenario 20 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

296

Scenario 21 8 1.4 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 6 1.0 0.8 4 0.6 0.4 2 0.2

0 0.0

1.4 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 2.0 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 Benthic blue green 1.5 1.0 0.8 1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 21. Simulated biomass dynamics for scenario 21 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

297

Scenario 22 8 1.4 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish Black bullhead (1+) Other benthivores 6 1.0 0.8 4 0.6 0.4 2 0.2

0 0.0

1.4 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 2.0 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 Benthic blue green 1.5 1.0 0.8 1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 22. Simulated biomass dynamics for scenario 22 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

298

Scenario 23 1.4 6 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish 5 Black bullhead (1+) Other benthivores 1.0 4 0.8 3 0.6 2 0.4

1 0.2 0.0

1.4 2.5 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8 0.6 1.0 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 2.0 Benthic blue green 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 23. Simulated biomass dynamics for scenario 23 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

299

Scenario 24 1.4 6 Common carp Black bullhead (0) Bluegill (0) Crappie Black bass Bluegill (1+) 1.2 Channel catfish Flathead catfish 5 Black bullhead (1+) Other benthivores 1.0 4 0.8 3 0.6 2 0.4

1 0.2 0.0

1.4 2.5 Darters Walleye (0) Bigmouth buffalo Yellow perch Esocids White bass 1.2 Minnows and shiners Yellow bass (0) 2.0 Walleye (1+) Sunfish 1.0 1.5 0.8 0.6 1.0 0.4 0.5 0.2 0.0 0.0

1.4 1.4 Yellow bass (1+) Benthic crustacea Benthic insects Other bivalves 1.2 Worms Benthic crustacea 1.2 Snails Copepod Chironomidae Zebra mussels

Relative biomass 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0

1.4 Cladaceran Benthic Algae Planktonic Algae Macrophytes Rotifer Planktonic blue green 1.2 2.0 Benthic blue green 1.0 1.5 0.8

1.0 0.6 0.4 0.5 0.2 0.0 0.0

0 1020304050 0 1020304050 Years

Figure 24. Simulated biomass dynamics for scenario 24 in Clear Lake, Iowa. Biomass is shown as a proportion of baseline level.

300

Scenario class: Baseline common carp and zebra mussel biomass Baseline commercial fishing mortality and Ventura Marsh loading

1.3 Crappie yield Sunfish yield Walleye (1+) yield Yellow perch yield 1.2 White bass yield Yellow bass (1+) yield 1.1 1.0 0.9 0.8 Baseline commercial fishing mortality and decresedVentura Marsh loading

1.3 1.2 1.1 1.0 0.9 0.8 Increased commercial fishing mortality and baseline Ventura Marsh loading 6 5

Relative yield fishery 4 3 2 1

Increased commercial fishing mortality and increased Ventura Marsh loading 6 5 4 3 2 1

0 1020304050 Years

Figure 25. Simulated fishery yield dynamics for recreationally harvested food web groups for scenario class 1 (baseline common carp and zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 1 (top) to 4 (bottom) respectively.

301

Scenario class: Baseline common carp and increasing zebra mussel biomass Baseline commercial fishing mortality and Ventura Marsh loading

Crappie yield Sunfish yield 2.0 Walleye (1+) yield Yellow perch yield White bass yield Yellow bass (1+) yield 1.5

1.0

0.5

0.0 Baseline commercial fishing mortality and decresedVentura Marsh loading

2.0

1.5

1.0

0.5

0.0 Increased commercial fishing mortality and baseline Ventura Marsh loading

6 5

Relative yield fishery 4 3 2 1 0 Increased commercial fishing mortality and increased Ventura Marsh loading

6 5 4 3 2 1 0 0 1020304050 Years

Figure 26. Simulated fishery yield dynamics for recreationally harvested food web groups for scenario class 2 (baseline common carp and increasing zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 5 (top) to 8 (bottom) respectively.

302

Scenario class: Baseline common carp and decreasing zebra mussel biomass Baseline commercial fishing mortality and Ventura Marsh loading 1.8 Crappie yield Sunfish yield Walleye (1+) yield Yellow perch yield 1.6 White bass yield Yellow bass (1+) yield 1.4 1.2 1.0

0.8 Baseline commercial fishing mortality and decresedVentura Marsh loading 1.8 1.6 1.4 1.2 1.0 0.8 Increased commercial fishing mortality and baseline Ventura Marsh loading 6 5

Relative yield fishery 4 3 2 1

Increased commercial fishing mortality and increased Ventura Marsh loading 6 5 4 3 2 1

0 1020304050 Years

Figure 27. Simulated fishery yield dynamics for recreationally harvested food web groups for scenario class 3 (baseline common carp and decreasing zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 9 (top) to 12 (bottom) respectively.

303

Scenario class: Increasing common carp and baseline zebra mussel biomass Baseline commercial fishing mortality and Ventura Marsh loading 1.4 Crappie yield Sunfish yield 1.2 Walleye (1+) yield Yellow perch yield 1.0 White bass yield Yellow bass (1+) yield 0.8 0.6 0.4 0.2 0.0 Baseline commercial fishing mortality and decresedVentura Marsh loading 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Increased commercial fishing mortality and baseline Ventura Marsh loading 1.4 1.2 1.0

Relative yield fishery 0.8 0.6 0.4 0.2 0.0 Increased commercial fishing mortality and increased Ventura Marsh loading 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

0 1020304050 Years

Figure 28. Simulated fishery yield dynamics for recreationally harvested food web groups for scenario class 4 (increasing common carp and baseline zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 13 (top) to 16 (bottom) respectively.

304

Scenario class: Increasing common carp and zebra mussel biomass Baseline commercial fishing mortality and Ventura Marsh loading 1.4 Crappie yield Sunfish yield 1.2 Walleye (1+) yield Yellow perch yield 1.0 White bass yield Yellow bass (1+) yield 0.8 0.6 0.4 0.2 0.0 Baseline commercial fishing mortality and decresedVentura Marsh loading 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Increased commercial fishing mortality and baseline Ventura Marsh loading 1.4 1.2 1.0

Relative yield fishery 0.8 0.6 0.4 0.2 0.0 Increased commercial fishing mortality and increased Ventura Marsh loading 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

0 1020304050 Years

Figure 29. Simulated fishery yield dynamics for recreationally harvested food web groups for scenario class 5 (increasing common carp and zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 17 (top) to 20 (bottom) respectively.

305

Scenario class: Increasing common carp and decreasing zebra mussel biomass Baseline commercial fishing mortality and Ventura Marsh loading 1.4 Crappie yield Sunfish yield 1.2 Walleye (1+) yield Yellow perch yield 1.0 White bass yield Yellow bass (1+) yield 0.8 0.6 0.4 0.2 0.0 Baseline commercial fishing mortality and decresedVentura Marsh loading 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Increased commercial fishing mortality and baseline Ventura Marsh loading 1.4 1.2 1.0

Relative yield fishery 0.8 0.6 0.4 0.2 0.0 Increased commercial fishing mortality and increased Ventura Marsh loading 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

0 1020304050 Years

Figure 30. Simulated relative fishery yield dynamics for recreationally harvested food web groups for scenario class 6 (increasing common carp and decreasing zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 21 (top) to 24 (bottom) respectively.

306

Scenario class: Baseline common carp and zebra mussel biomass

Baseline commercial fishing mortality and Ventura Marsh loading

Total suspended solids Total excretion 1.3 Secchi transparency Total phosphorus

1.2

1.1

1.0

0.9

0.8 Baseline commercial fishing mortality and decresedVentura Marsh loading

1.3

1.2

1.1

1.0

0.9

0.8 Increased commercial fishing mortality and baseline Ventura Marsh loading

1.3 Relative value 1.2

1.1

1.0

0.9

0.8

Increased commercial fishing mortality and increased Ventura Marsh loading

1.3

1.2

1.1

1.0

0.9

0.8

0 1020304050 Years

Figure 31. Simulated dynamics for chemical/physical water quality variables of scenario class 1 (baseline common carp and zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 1 (top) to 4 (bottom) respectively.

307

Scenario class: Baseline common carp and increasing zebra mussel biomass

Baseline commercial fishing mortality and Ventura Marsh loading

Total suspended solids Total excretion 1.3 Secchi transparency Total phosphorus

1.2

1.1

1.0

0.9

0.8

Baseline commercial fishing mortality and decresedVentura Marsh loading

1.3

1.2

1.1

1.0

0.9

0.8

Increased commercial fishing mortality and baseline Ventura Marsh loading

1.4 Relative value

1.2

1.0

0.8

Increased commercial fishing mortality and increased Ventura Marsh loading

1.4

1.2

1.0

0.8

0 1020304050 Years

Figure 32. Simulated dynamics for chemical/physical water quality variables of scenario class 2 (baseline common carp and increasing zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 5 (top) to 8 (bottom) respectively.

308

Scenario class: Baseline common carp and decreasing zebra mussel biomass

Baseline commercial fishing mortality and Ventura Marsh loading

Total suspended solids Total excretion 1.3 Secchi transparency Total phosphorus

1.2

1.1

1.0

0.9

0.8 Baseline commercial fishing mortality and decresedVentura Marsh loading

1.3

1.2

1.1

1.0

0.9

0.8 Increased commercial fishing mortality and baseline Ventura Marsh loading

1.3 Relative value 1.2

1.1

1.0

0.9

0.8 Increased commercial fishing mortality and increased Ventura Marsh loading

1.3

1.2

1.1

1.0

0.9

0.8

0 1020304050 Years

Figure 33. Simulated dynamics for chemical/physical water quality variables of scenario class 3 (baseline common carp and decreasing zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 9 (top) to 12 (bottom) respectively.

309

Scenario class: Increasing common carp and baseline zebra mussel biomass

Baseline commercial fishing mortality and Ventura Marsh loading

Total suspended solids Total excretion 8 Secchi transparency Total phosphorus

0 Baseline commercial fishing mortality and decresedVentura Marsh loading

0 Increased commercial fishing mortality and baseline Ventura Marsh loading 7 6 Relative value 5 4 3 2

Increased commercial fishing mortality and increased Ventura Marsh loading 7 6 5 4 3 2 1

0 1020304050 Years

Figure 34. Simulated dynamics for chemical/physical water quality variables of scenario class 4 (increasing common carp and baseline zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 13 (top) to 16 (bottom) respectively.

310

Scenario class: Increasing common carp and zebra mussel biomass

Baseline commercial fishing mortality and Ventura Marsh loading

Total suspended solids Total excretion 8 Secchi transparency Total phosphorus

0 Baseline commercial fishing mortality and decresedVentura Marsh loading

0 Increased commercial fishing mortality and baseline Ventura Marsh loading 7

6 Relative value 5 4

3 2

Increased commercial fishing mortality and increased Ventura Marsh loading 7 6 5 4 3 2 1

0 1020304050 Years

Figure 35. Simulated dynamics for chemical/physical water quality variables of scenario class 5 (increasing common carp and zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 17 (top) to 20 (bottom) respectively.

311

Scenario class: Increasing common carp and decreasing zebra mussel biomass

Baseline commercial fishing mortality and Ventura Marsh loading

Total suspended solids Total excretion 8 Secchi transparency Total phosphorus

0 Baseline commercial fishing mortality and decresedVentura Marsh loading

0 Increased commercial fishing mortality and baseline Ventura Marsh loading 7 6 Relative value 5 4 3 2 1

Increased commercial fishing mortality and increased Ventura Marsh loading 7 6 5 4 3 2 1

0 1020304050 Years

Figure 36. Simulated dynamics for chemical/physical water quality variables of scenario class 6 (increasing common carp and decreasing zebra mussel biomass) in Clear Lake, Iowa. Yield is shown as a proportion of baseline level. Five food web groups are represented in each panel. Each panel row represents scenario 21 (top) to 24 (bottom) respectively.