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The role of pumpkinseed sunfish in structuring snail assemblages in northern Wisconsin lakes

Klosiewski, Steven Paul, Ph.D. The Ohio State University, 1991

UMI 300 N. Zeeb Rd. Ann Arbor, MI 48106

THE ROLE OF PUMPKINSEED SUNFISH IN STRUCTURING

SNAIL ASSEMBLAGES IN NORTHERN WISCONSIN LAKES

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of the Ohio State University

By

Steven Paul Klosiewski, B.S., M.S.

The Ohio State University

1991

Dissertation Committee: Approved by

R. A. Stein

J. Bart

W. M. Masters Department of Zoology ACKNOWLEDGEMENTS

Roy Stein contributed to many aspects of this work from its development to its completion. Jim Klosiewski, Barry Benson, Beth Campbell, and Mark

Humpert provided assistance in the field and processed most of the snail samples. Julie Ballinger counted and measured the majority of snails. Jim,

Norbert, and John Klosiewski helped build the cages; the Cesarz Drywall

Company provided the tools and warehouse space for cage building. Jon Bart and W. Mitch Masters provided constructive criticism on various drafts of this dissertation. This study was funded by U.S. National Science Foundation grant BSR85-00772 to Roy A. Stein. VITA

April 25, 1955 ...... Born - Milwaukee, Wisconsin

1978 ...... B.S., University of Wisconsin - Stevens Point, Stevens Point, Wisconsin

1979-1981 ...... Graduate Research Assistant, Department of Zoology, The Ohio State University, Columbus, Ohio

1981 ...... M.S., Department of Zoology, The Ohio State University, Columbus, Ohio

1981-1983...... Research Technician, Savannah River Ecology Laboratory, Aiken, South Carolina

1983-1988 ...... Graduate Research and Teaching Assistant, Department of Zoology, The Ohio State University, Columbus, Ohio

1988-Present ...... Wildlife Biologist, U.S. and Wildlife Service, Marine and Coastal Project, Anchorage, Alaska

PUBLICATIONS

Carline, R.F., and S.P. Klosiewski. 1985. Responses of fish populations to mitigation structures in two small channelized streams in Ohio. North American Journal of Fisheries Management 5:1-11. Lodge, D.M., K.M. Brown, S.P. Klosiewski, R.A. Stein, A.P. Covich, B.K. Leathers, and C. BrOnmark. 1987. Distribution of freshwater snails: spatial scale and the relative importance of physicochemical and biotic factors. American Malacological Bulletin 5:73-84.

Bart, J., and S.P. Klosiewski. 1989. Use of presence-absence to measure changes in avian density. Journal of Wildlife Management 53:847-852.

FIELDS OF STUDY

Major Fields: Zoology TABLE OF CONTENTS

ACKNOWLEDGEMENTS...... ii

VITA ...... iii

LIST OF TABLES...... vii

LIST OF FIGURES...... ix

INTRODUCTION ...... 1

CHAPTER PAGE I. PUMPKINSEED SUNFISH PREFERENCE FOR SNAILS: AN EVALUATION OF CURRENT FORAGING MODELS...... 5 Introduction...... 5 Methods...... 8 Results ...... 14 The act of ...... 14 Snail shell characteristics ...... 14 Single-snecies experiments...... 17 Prev profitabilities...... 23 Multispecies experiments- equal sizes and densities ...... 33 Multispecies experiments - varying sizes and densities ...... 38 Effect of habitat structural complexity ...... 42 Discussion...... 47 The role of size ...... 47 Relation to models of prev selection...... 50 Implications for the fie ld...... 54 Literature Cited...... 56

II. EVALUATING AN OPTIMAL FORAGING MODEL INCORPORATING CAPTURE SUCCESS AND SIMULTANEOUS ENCOUNTERS WITH PREY ...... 60 Introduction...... 60 The Pumpkinseed Sunfish-Snail Interaction ...... 62 The M odel...... 63 Materials and Methods ...... 71 Results ...... 74 Model Simulations...... 74 Comparison with results from laboratory experiments...... 78 Discussion...... 82 Summary...... 89 Literature Cited...... 90

III. PISH PREDATION AND ITS ROLE IN STRUCTURING SNAIL ASSEMBLAGES IN NORTHERN WISCONSIN LAKES ...... 92 Introduction...... 92 Methods...... 96 Results ...... 103 The snails ...... 103 Response of snail assemblages to predator density manipulations...... 109 High pumpkinseed density la k e...... 109 Low pumpkinseed density lake ...... 115 Discussion...... 125 Literature Cited...... 130

LIST OF REFERENCES...... 135

APPENDICES...... 144 Appendix A ...... 144 Appendix B ...... 147 LIST OF TABLES

TABLE PAGE

1. Regressions of crushing resistance versus snail size based on snails collected during 1984-1987. Cr=a+b'L for single prey type experiments and Cr=aLb for multiple prey type experiments. L is snail length (mm). Regressions for Helisnmn ancens and Oannnelnnrm dedsum were based on data from more than one year because of small sample sizes within years...... 16

2. Regressions of handling times as a function of fish size and snail crushing resistance for snails that were consumed. Htsa+bi’C-ba’FC, where Ht is handling time (s), F is fish length (mm), and C is crushing resistance (N). Significance levels are in parentheses...... 26

3. ANOVA tables for regressions of handling times, of rejected snails, on snail mass, crushing resistance, and fish length. SS= partial sums of squares...... 29

4. Regressions of snail mass (dry mass excluding the shell) versus snail length for limosa. Lvmnaea emnrcrinntfl. Helisoma ancens. and Phvsa sp. M=aLb, where M=snail mass (g) and L=snail length (mm)...... 30

5. ANOVA results testing the effect of relative abundance of 3-mm Amnicola limosa and 3-, 6-, or 9-mm Lvmnaea emareinata on attack and diet preferences for 90-, 120-, and 150-mm pumpkinseed sunfish. For each comparison, the ratio of Amnicola:Lvmnaea was 50:50, 75:25, 90:10, and 270:10 ...... 41

6. Model parameters used in simulations of 90-150 mm pumpkinseed sunfish foraging on 3-mm Amnicola limosa and Lvmnaea emareinata. Estimate of swimming speed comes from Mittelbach 1981; all other parameters are from Chapter I. PS * pumpkinseed sunfish...... 72 7. Maximum size, i.e., length and mass, of snail taxa, collected in M ann and Round Lakes, Wisconsin. Maximum length was measured along the axis of the shell’s largest dimension...... 104

8. Crushing resistance for snails of a given size estimated from regressions of crushing resistance on dry body mass and length of maximum shell dimension (from Osenberg and Mittelbach 1989). Species are ranked based on shell strength, from weakest to strongest, for snails with dry body mass equal to 0.5 mg; similarly, ranks are provided for snails with dry body mass equal to 10 mg and for snails with shell size equal to 4 and 10 mm. . . . 108

9. Repeated-measures ANOVAs done on log10(x + 1) densities of snails in exdosures (Exc), enclosures (Enc), and cageless controls (C) in Mann Lake, Wisconsin, i.e., the high pumpkinseed density lake. Comparisons between high* and no*predator treatments, i.e., C and Enc vs Exc, and within high-predator treatments, i.e., C vs Enc, were done using orthogonal contrasts. P values were calculated using e-adjusted error degrees of freedom (Huynh and Feldt 1976) 112

10. Repeated-measures ANOVAs done on log10(x + 1) densities of snails in exdosures (Exc), enclosures (Enc), and cageless controls (C) in Round Lake, Wisconsin, i.e., the low pumpkinseed density lake. Comparisons between low- and high-predator treatments, i.e., C and Exc vs Enc, and within low-predator treatments, i.e., C vs Exc, were done using orthogonal contrasts. P values were calculated using E-adjusted error degrees of freedom (Huynh and Feldt 1976), 116

11. Repeated-measures ANOVAs done on log10(x + 1) densities of snails in exclosures, enclosures, and cageless controls in Round Lake, Wisconsin, i.e., the low pumpkinseed density lake. Fish vs No Fish compares controls and yellow perch and pumpkinseed endosures to exclosures; PS vs No PS compares controls and pumpkinseed enclosures to yellow perch endosures; High PS vs Low PS compares pumpkinseed enclosures to controls. P values were calculated using E-adjusted error degrees of freedom (Huynh and Feldt 1976). 121

viii LIST OF FIGURES

FIGURE PAGE

1. C rushing resistance ofPhvsa sp.,I .vm naea emareinata. Amnicola limosa. Helisoma anceps. and Campeloma decisum as a function of snail size. Snails were collected from northern Wisconsin lakes during summers, 1984-1987...... 15

2. Attack and diet preferences of pumpkinseed (90-, 120-, and 150-mm TL) foraging in an arena with equal numbers of 2- and 4-mm Amnicola limosa on a simulated sand habitat. Means and 95% confidence intervals (vertical bars) were based on trials using n different fish. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference...... 18

3. Proportion of snails rejected by pumpkinseeds (90-, 120-, and 150-mm TL) when foraging in an arena with equal numbers of 2- and 4-mm Amnicola limosa or 3-, 6-, 9-, and 12-mm T.vmnaoa emareinata on a simulated sand habitat. Vertical bars are means and 95% confidence intervals. Numbers above the bars show the numbers of fish used to calculate means. ND is no data ...... 20

4. Attack and diet preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging in an arena with equal numbers of 3-, 6-, 9-, and 12-mm Lvm naea emareinata on sand. Means and 95% confidence intervals (vertical bars) were based on trials using n individual fish. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference 21

5. Pumpkinseed handling times as a function of fish size (90-, 120-, and 150-mm TL) and snail size for snails that were consumed. Points represent means from individual fish that were allowed to forage on only one size of (closed circles) or equal numbers of different sizes of (open squares), Amnicola limosa or Lvmnaea emareinata in a sand habitat ...... 25 6. H andling times for rejected snails as a function of pumpkinseed size (90-, 120-, and 150-mm TL), snail size, and snail species. Points represent means from individual fish that were allowed to forage on only one size of (closed circles) or equal numbers of different sizes of (open squares) Amnicola limosa or Lvmnaea emareinata in a sand habitat ...... 28

7. Plot of predictions and residuals from a regression of crushing resistance and fish size on the probability that a snail would be rejected. P = 1 / [ 1 + exp (a - bx • CR + b2 • CR • FL) ] where P is the probability that a snail will be rejected, CR is snail crushing resistance, and FL is fish length. Estimated parameters, a = 3.17, ba = 1.13, and b2 = 0.006, all differed from zero (p<0.05)...... 31

8. Attack and diet preferences by 120- and 150-mm pumpkinseeds foraging in an arena with equal numbers of 6-mm Lvmnafia emarerinata. Helisoma ancens. and Camneloma decisum. Vertical bars represent means and 95% confidence intervals; n is the number of fish used in calculations. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference...... 34

9. Attack and diet preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging in an arena with equal numbers of 4.5-mm Helisoma ancens and 6-mm Phvsa an.. Lvmnaea emarerinata. and Helisoma ancens. Trials were done in a sand habitat. Means and 95% confidence intervals (vertical bars) were based on trials using n individual fish. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference...... 37

10. Attack and diet preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging on varying densities and sizes of Amnicola limosa and Lvmnaea emareinata in a sand habitat. Vertical bars represent means and 95% confidence intervals. Numbers above each set of bars represent the number of fish used to calculate means. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference...... 40

11. Comparison of attack preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging in an arena containing equal numbers of 2- and 4-mm Amnicola limosa in sand and simulated macrophyte habitats. Vertical bars represent means and 95% confidence

x intervals. Attack preferences in sand habitats were presented previously in Fig. 2...... 44

12. Comparison of attack preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging in an arena containing equal numbers of 3-, 6-, 9-, and 12-mm Lvmnaea emareinata in sand, artificial macrophyte, macrophyte, and cobble habitats. Vertical bars represent means and 95% confidence intervals. Attack preferences for the sand habitat were presented previously in Fig. 4...... 45

13. Schematic diagram of the spatial relationship between a pumpkinseed sunfish in the water column (distributed in three dimensions) and a potential snail prey on a sand substrate (distributed in two dimensions). Pursuit distance z can be calculated as z = (X2 + y2)*. See text for a description of parameters...... 64

14. Predicted proportion of 3-mm Lvmnaea attacked and attack preference for Lvmnaea as a function of proportion of Lvmnaea. relative to Amnicola. in the environment, pumpkinseed sunfish (PS) size, and total snail abundance ...... 75

15. Predicted proportion of Lvmnaea attacked and attack preference for Lvmnaea as a function of Amnicola abundance in the environment, pumpkinseed sunfish (PS) size, and Lvmnaea abundance ...... 77

16. Predicted proportion of attacks on and preference for a hypothetical snail type as a function of its relative abundance in the environment for the situation where pc=1.0, f* = 5 s, ffcc=15 s, e}=l mg, and e2=2 mg. See Table 6 for parameter descriptions. . . 79

17. Observed and predicted proportion of Lvmnaea attacked and attacked preference for Lvmnaea as a function of proportion of Lvmnaea in the environment and pumpkinseed sunfish (PS) size. Open squares represent model predictions and closed circles and vertical bars represent observed means and 95% confidence intervals...... 80

18. Mean size of snails (± 1 SD) collected in the high pumpkinseed density lake, Mann Lake, Wisconsin, 1986-1987 ...... 105

xi 19. Mean size of snails (± 1 SD) collected in the low pumpkinseed density lake, Round Lake, Wisconsin, 1986-1987 ...... 106

20. Snail species composition, based on proportion of total biomass estimated from samples at the start of the experiment (May 1986), in the low pumpkinseed density lake, Round Lake, and the high pumpkinseed density lake, Mann Lake, Wisconsin. PEX = Promenetus exacuous. HCA = Helisoma camoanulatn. HAN = Helisoma ancens. PHY = Phvsa sp., GDE = Gvraulus deflectus. MLU = Marstonia lustrica. VTR = Valvata tricarinata. AWA = . ALI = Amnicola limosa. and GPA = Gvraulus parvus...... 110

21. Mean abundance of snails in enclosures, exclosures, and controls in Mann Lake, HPDL, 1986-1987. ** indicates intervals when exclosures differed from enclosures and controls, * indicates intervals when enclosures differed from controls...... 114

22. Mean abundance of snails in enclosures, exclosures, and controls in Round Lake, LPDL, 1986-1987. ** indicates intervals when exclosures and controls differed from enclosures, * indicates intervals when exdosures differed from controls...... 119 INTRODUCTION

That predators are important in structuring communities is well

supported by theoretical models (Cramer and May 1972, Roughgarden and

Feldman 1975, Caswell 1978), experimental studies (Connell 1970, Paine 1974,

Morin 1983,1984, Carpenter et al. 1987), and surveys of natural communities

(Brooks and Dodson 1965, Wells 1970). Predator-prey studies in a variety of

marine, brackish, and freshwater systems demonstrate that predation can be

an important force in influencing prey behavior (Peckarsky 1980, Gilliam

1989), distribution (Peckarsky and Dodson 1980, Zaret and SufFem 1976), and

abundance (Crowder and Cooper 1982). The best studies demonstrating that

predators control prey diversity and abundance come from the rocky intertidal

zone and freshwater lakes (Sih et al. 1985). In freshwater, the best evidence

supporting the role of predators as a major structuring force comes from

pelagic zooplankton communities (Brooks and Dodson 1965, Carpenter et al.

1987). Work on freshwater amphibian communities similarly demonstrates

that predators regulate prey communities (Morin 1983, Alford 1989).

Relatively few studies have been conducted in freshwater benthic systems, and

results are conflicting regarding the role of predators. Of the 26 freshwater

lentic studies reviewed by Sih et al. (1985), seven were done on benthic invertebrates. The negative results of Thorp and Bergey (1981a, 1981b) are at the center of the debate on the importance of predation in benthic freshwater systems. Some argue, that because Thorp and Bergey did not document fish in their controls, their study may not be a good refutation of the importance of predation (Crowder and Cooper 1982). Results from more recent work

(Cooper et al. 1990, Hall et al. 1990) suggest that the large mesh size used by

Thorp and Bergey may have contributed to their negative findings as well. In another study, Gilinsky (1984) found that excluding predators from areas in a North Carolina pond resulted in changes in the invertebrate community; however, her conclusions are not strong because she failed to provide evidence that the affected species were actually preyed upon by the predator. Also, because she first removed predators from cages in August, after placing the cages in the pond in March, her results may be due to predator densities in cages from March to August rather than the result of predator manipulation; because she did not use a repeated-measures design, the results would be expected to be strongly influenced by initial starting densities of invertebrates.

Based on my review of the literature, before the start of my study, I concluded that the equivocal results in freshwater systems were due to inadequacies in experimental design rather than lack of a predator effect.

As a result, I examined the importance of predation by pumpkinseed

sunfish (Lenomis gibbosus) as a structuring force on the benthic snail assemblages in northern Wisconsin lakes. I chose pumpkinseeds and snails because 1) there exists a variety of snail species with differing shell characteristics (that presumably determined vulnerability to pumpkinseed); 2) snails are eaten by a diverse group of predators, including and invertebrates, yet no consumers within these groups are specialist molluscivores like pumpkinseeds; 3) snails are differentially susceptible to pumpkinseeds owing to variable and easily quantifiable shell characteristics

(e.g., shell strength, Stein et al. 1984); 4) predator avoidance behaviors by snails are simple or nonexistent; 5) unlike aquatic insects, snails are relatively easy to quantify in the field and identify to species; 6) competition and abiotic factors appear less important in determining dominance by one snail species than predation (Lodge et al. 1987); and 7) snails are easy to collect, hold, and manipulate in laboratory and field experiments.

I took a multi-faceted approach to determining if pumpkinseeds influence snail assemblage structure: 1) I quantified snail shell characteristics that influenced pumpkinseed choice of snails, and I performed laboratory experiments to determine size- and species-selection by pumpkinseed under a variety of experimental conditions (Chapter I); 2) 1 developed a foraging model

to further explore how pumpkinseeds might choose snails in the field

(Chapter II); and 3) I conducted field-cage experiments to determine if predator induced changes in the field were supported by predictions from laboratory snail choice experiments and model predictions (Chapter III). CHAPTER I PUMPKINSEED SUNFISH PREFERENCE FOR SNAILS: AN EVALUATION OF CURRENT FORAGING MODELS

Introduction

By selectively preying on individuals or species with specific

characteristics, predators can substantially alter and control the structure of communities (Brooks and Dodson 1965, Paine 1966, 1974, Zaret and Paine

1973, Zaret 1980). In this regard many studies have demonstrated that prey

abundance (Werner and Hall 1974, Murdoch et al. 1975, Stamps et al. 1981,

Bres 1986), behavior (Zaret and SufFem 1976), structural defenses (Elner and

Hughes 1978, Stein et al. 1984, Palmer 1985, Osenberg and Mittelbach 1989),

and size (Werner and Hall 1974, Stein 1977, Zaret 1980, Eggers 1982, Juanes

and Hartwick 1990, Palmer 1990) influence a predator’s choice of prey. In an

effort to generalize behavior of selective predators, mathematical models have

been developed to predict prey choice and examine how prey abundance and

size, or one of their correlates, determine a predator’s diet.

As part of a large study to determine if predators structure snail

assemblages in lakes, I examined the interaction between pumpkinseed sunfish

(Lepomis gibbosus) and snails. In aquatic systems, particularly in freshwater

5 lakes, the evidence for size structured interactions is well established (Stein et al. 1988). Prey size clearly influences predator choice (see Zaret 1980 for a review). For fishes, most studies show that predators choose large over small prey (e.g., Werner and Hall 1974, Mittelbach 1981), (but see Hansen and Wahl

1981, Bence and Murdoch 1986). Limits to the largest prey consumed generally depend on gape size (Zaret 1980, Unger and Lewis 1983). Below these limits, profitabilities increase with prey size (Bence and Murdoch 1986), thus predators choose large over small prey.

However, for predators that consume molluscs, shell strength, independent of size, influences vulnerability to, and preference by, a variety of predators. Shell-breaking predators, such as , prefer thin-shelled over thick-shelled snails (Elner and Hughes 1978, Palmer 1986). Freshwater molluscivorous fishes choose snails based, at least in part, on shell strength

(Stein et al. 1984, Osenberg and Mittelbach 1989). Costs associated with

eating snails includes time and energy spent handling and crushing snails that

are either consumed or rejected (Stein et al. 1984, Osenberg and Mittelbach

1989).

Prey choice by freshwater fishes and the role of prey size and abundance

has been modelled extensively (Werner and Hall 1974, O’Brien et al. 1976,

Mittelbach 1981, Osenberg and Mittelbach 1989). The reactive field volume model (RFVM, Werner and Hall 1974), the apparent size model (ASM, O’Brien et al. 1976), and models based on optimal foraging theory (OFT, e.g.,

Mittelbach 1981) have all been used successfully to explain prey choice in the field and laboratory. In general the overwhelming prediction from tests of these models is that predators should choose large over small prey. Although this agrees well with many studies of prey selection, the tests and predictions have been strongly influenced by the range of prey sizes and abundances used in experiments. In fact, determining which model best predicts choice has been uncertain (O’Brien et al. 1976, Butler and Bence 1984, Wetterer and

Bishop 1985, Bence and Murdoch 1986).

Because of the preponderance of evidence suggesting the importance of snail size and shell strength, my approach included experiments in which the sizes and species of snails, and thus shell strengths, were varied. Additionally, predator size, prey abundance, and habitat heterogeneity were manipulated.

And, given the wide range of experimental conditions in this study, I used my results to generate qualitative comparisons with models of prey choice. 8 Methods

To understand how shell characteristics influence pumpkinseed preference for snails, I conducted laboratory experiments in which sizes, species, and densities of snails were varied. I chose snail species that were common in northern Wisconsin lakes and that presented pumpkinseeds with a wide range of snail sizes with differing shell strengths from which to choose.

Snails and pumpkinseed sunfish were collected from lakes located within

30 km of the University of Wisconsin’s Trout Lake Station, Vilas County,

Wisconsin.

Shell strength, i.e., crushing resistance, was measured with a pancake load cell, connected to a chart recorder, and mounted to a small vise.

Operationally, crushing resistance was defined as the minimum force needed to crush a snail’s shell. This was determined by placing a snail between the load cell and vise, and slowly turning the vise handle until the snail was crushed.

Snail size was measured along the axis of the largest shell dimension as in Stein el al. (1984). Snails used to determine crushing resistance were measured to nearest 0.1 mm. Those used in selection experiments were divided into 1-mm size classes, hereafter referred to by the lower limit for a given size range.

Experiments were done during 3 years using 72 pumpkinseeds distributed almost evenly among three size classes: i.e., 90-100, 120-130, and

150-165 Tum TL. Pumpkinseeds <=90 mm eat few snails (Mittelbach 1984); those >165 mm were uncommon in my lakes (Klosiewski and Stein, unpublished data). Fish were held in separate 40-L aquaria at 18-21 C with

a 16 h light:8 h dark photoperiod and maintained on a diet of snails.

Experiments were conducted in two tanks, each with two holding areas

at both ends of a 100 cm x 42 cm x 33 cm centrally located experimental arena.

The experimental arena was accessible to each holding area through a sliding

door. To simulate a natural background against which snails were

encountered by the predator and to determine how habitat heterogeneity

might influence preference for snails I created sand, macrophyte, and cobble

habitats. Sand habitats were created by gluing a thin layer of sand to the tank

bottom. In so doing, all snails were accessible to the predator, allowing us to

measure pumpkinseed preference without the confounding effects of

differential availability through burial. Macrophyte habitats were created in

two ways: 1) strands of polypropylene rope (459 stems/m2), i.e., artificial

macrophytes ala Savino and Stein (1982), were glued to a sand-covered bottom, 10 and 2) strands of freshly collected Meeralodonta beckii (225 stems/m2) were inserted into slits cut in a sand covered plastic mat. For cobble habitat, cobble was placed on the tank bottom.

Before a trial, each fish was satiated with the sizes and species of snails to be used in that trial, then placed in a holding area. Initially, fish were starved for 8 h; later, they were starved for 2 h after I found that snail selection did not differ between the two. Before a fish was released from the holding area, snails were scattered across the tank bottom, allowed to right themselves and disperse for about 5-10 min. In macrophyte experiments snails were given about 30 min to colonize the artificial or real macrophytes.

Trials lasted for 10 min or until all the snails of a given type, i.e., size and species, were eaten or until 25-30% of all snails were eaten. During a trial, an observer, with the aid of an event recorder, monitored sizes and

species of snails attacked and whether the snail was consumed or rejected.

Additionally, the event recorder allowed us to keep track of handling times for

crushed and rejected snails, and the order of prey selection.

Pumpkinseed preference for snails was measured in two ways. For

snails that were attacked, i.e., picked up, but not consumed by the predator,

I used Bence and Murdochs’ (1986) beta. Manly’s (1980) alpha was used for 11 snails that were actually included in a pumpkinseed’s diet. Both preference measures account for order of selection and prey depletion. Hereafter, unless noted otherwise, diet preference refers to preference based on diets and attack preference refers to preference based on attacks. Significance of preference was based on tests against random selection (Chesson 1983). Under random selection, the expected value for each prey type equals the reciprocal of the number of prey types (Chesson 1983); thus, I subtracted the expected value from the observed preference value and tested this difference against zero.

Because preference variance-covariance matrices are singular, I excluded one prey type from each analysis (Chesson 1983). Thus, for two prey types I used a t-test; otherwise I used Hotelling’s T2. When fish never attacked or consumed a given prey type I had to exclude that prey type plus another prey type to prevent singular error matrices. In these cases, tests against random selection should be conservative given that the type most strongly selected against was not included in analyses. Comparisons between attack preference and preference based on diets were done by subtracting the alpha vector from the beta vector and using a t-test (two prey types) or Hotelling’s T2 (> 2 prey types) to test if the difference between attack and diet preference was equal to

zero.

Four types of experiments were performed: 1) single-species experiments

in which fish were given a choice of equal numbers of different sizes of snails, 12 2) multi-species experiments in which fish were given a choice of equal numbers of different species of snails of a similar size, 3) multi-species experiments in which the sizes and densities of different snail species were manipulated, and 4) experiments that allowed comparisons among habitats.

For more detail on snail sizes, species, and densities, and the number of replicates per experiment, see Appendix A.

In a set of independent trials, I measured handling times for rejected and consumed snails. Here, fish were allowed to forage on 100 snails of only one prey type. Handling time included the interval from the time a snail was picked up until the time the snail was swallowed or until the snail was spit out intact. The number of snails consumed and rejected also was recorded. These data were used to estimate prey profitabilities.

Profitabilities, ala Bence and Murdoch (1986), were calculated as

p_ M Hte + (Pr/Pe )Htr where M is the dry mass of an individual snail, Hte is the time spent handling

snails that were consumed, Ht,. is the time spent handling snails that were

rejected, Pe is the probability that a prey item will be consumed rather than

rejected, and Pr is the probability that a prey item will be rejected. Relative

profitabilities were calculated for display on preference graphs by dividing the 13 profitability of a prey type by the sum of the profitabilities of all the prey types within an experiment.

Snail dry mass, excluding the shell, was measured by anesthetizing snails in water containing menthol crystals, then dissolving the shells in 25%

HC1. Snails were rinsed in water and dried for 24 h at 60 C. 14 Results

The act of predation

When pumpkinseed sunfish preyed on snails they consumed only the

soft body parts. First, fish picked up a snail, then they moved the snail to their pharyngeal area and tried to crush the snail’s shell. If the shell was crushed, the fish spit out the shell fragments, swallowed the soft body parts, and proceeded to another snail. Not all snails were crushed; at times snails were spit out whole and apparently unharmed. Most pumpkinseeds handled

snails one by one, but occasionally pumpkinseeds tried to crush two snails

simultaneously.

Snail shell characteristics

Crushing resistance increased with snail size for all species (Fig. 1). For

the size range of snails susceptible to pumpkinseed predation (snails < = 13

mm), crushing resistance varied between 0.3 and 100 newtons (N). Among

species, marked differences between similar-sized snails existed. Phvsa sp.

clearly had the weakest shells with a maximum crushing resistance < 40 N.

For snails of a given size, order of crushing resistance was Phvsa sp., Lvmnaea

emarginata. and Helisoma ancens (Table 1). The strongest shells belonged to

Amnicola limosa and Campeloma decisum. If pumpkinseed sunfish choose 15

1000.0 1000.0 oPIum L 5/84 oTrout L. 8 /8 4 opium L 8/84 oGrassy L 8/84 * i a Round L 8/84 □Fishtrap L 5/84 ^ a ! 100.0 oTrout L 8/84 o Erickson L 8 /8 4 a A 7 f100.0 * «

10.0 ■ 10.0 □ j i ta * n»»«rVb a 1.0 ■■1.0 Helisoma Campeloma 0.1 0.1 oFishtrap L 5/84 oPIum L 5 /8 4 oRound L. 8/87 oTrout L 5 /8 4 □ Mann ^ 5 / 8 4 ^ 10.0 ®a A 10.0 a t c01 e 3 ®o o * * v 0) z* 1.0 1.0 0) u Amnlcola Amnicola a 0 c 1 0.1 0.1 o a t % "in oTrout L 8/84 oTrout L 8 /8 7 *55 V) oTrout L 8/85 oWoodruff Fish a) cc Hatchery 8 /8 4a ®, * Q£ O' 10.0 10.0 c !E IE co 3w s k . o 1.0 1.0 a

Lymnaaa Lymnaeo 0.1 0.1 o Fish trap L. 5/84 Phyaa oAllequash L 8 /8 4 Physa *Plum L 5/84 oRound L 8 /8 4 □Plum L 8/84 □Trout L- 88/84 /8 4 • o 10.0 cfb1 10.0 D* Ut « cf® o 8 A 1.0 □ A 1.0 s°

0.1 0.1 10 10 Snail Length (mm)

Figure 1. Crushing resistance of Phvsa sp., Lvmnaea emarcrinata. Amnicola limosa. Helisoma ancens. and Campeloma deriaum as a function of snail size. Snails were collected from northern Wisconsin lakes during summers, 1984-1987. 16 Table 1. Regressions of crushing resistance versus snail size based on snails collected during 1984-1987. Cr=a+bL for single prey type experiments and Cr=aLb for multiple prey type experiments. L is snail length (mm). Regressions for Helisoma ancens and Campeloma decisum were based on data from more than one year because of small sample sizes within years.

a b if f i n

Single prev type experiments

Amnicola limosa (1987) -2.12 3.89 0.49 0.004 15

Lvm naea emarginata (1987)-6.74 2.37 0.84 0.0001 28

Multiple prev type experiments

Amnicola limosa (1985) 2.571 0.723 0.15 0.001 96

Lvmnaea emarginata (1985) 0.201 2.033 0.87 0.0001 110

Lvmnaea emarginata (1986) 0.193 2.025 0.85 0.0001 138

Helisoma anceps 0.276 1.988 0.84 0.0001 41

Campeloma decisum 0.710 1.802 0.58 0.0001 40

Physa sp. 0.147 1.821 0.52 0.0001 123 17 snails based on shell strength, I would expect them to be both size and species selective.

Snail size, independent of any correlation with shell strength, also should influence snails’ vulnerability due to gape limitation of the predator.

In this regard, only Amnicola limosa (=5 mm maximum length) of all sizes is vulnerable to all sizes of pumpkinseeds. In the Wisconsin lakes, all other taxa reach a size refuge. In my experiments, only Lvmnaea emarginata escaped predation due to predator gape limitation.

Single-species experiments

When presented with equal numbers of 2- and 4-mm Amnicola limosa

on sand, pumpkinseeds showed strong preferences that varied with the size of both predator and prey (Fig. 2). Small pumpkinseeds (90-100 mm TL)

consumed only small Amnicola limosa , whereas large pumpkinseeds (150-165

m m TL) ate more large Amnicola than small ones (t-test, p=0.0001).

Preferences of medium-sized fish (120-130 mm TL) were intermediate to the

other two size classes and were not different from random (t-test, p=0.24);

however, this does not imply that medium-sized fish were not making choices. 18

1.0- • 1.0 90mm PS 0 .8 - n=5 ■ 0.8

0 .6 - • 0.6

0.4- ■0.4

0 .2 - - 0.2 0

0.0- — t - - 0.0 1.0- ■ 1.0 0 .2 - ■ 0.2 b

0.0- - 0.0 1.0- - 1.0 150mm PS 0 .8 - n=6 - 0.8

0 .6 - - 0.6

0.4 -0.4

0.2 - 0.2

0.0 - 0.0 4 2 Snail Length (mm)

Figure 2. Attack and diet preferences of pumpkinseed (90-, 120-, and 150-mm TL) foraging in an arena with equal numbers of 2- and 4-mm Amnicnla limnas on a simulated sand habitat. Means and 95% confidence intervals (vertical bars) were based on trials using n different fish. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference. 19 All sizes of pumpkinseeds attacked more snails than were included in their diet (see Appendix A), but diets did not result from fish indiscriminately attacking snails as encountered, then rejecting those they could not crush.

Instead, large and small fish exhibited attack preferences that differed from random (t-test, p=0.003, 90-mm PS; p=0.0001,150-mm PS) and that tended to be skewed toward large snails when compared to diet preferences (t-test, p=0.13,90-mm PS; p=0.068,150-mm PS). Attack preferences for medium-sized fish did not differ from random (t-test, p=0.52), but attacks were skewed toward large snails when compared to diet preference (t-test, p=0.0004). All sizes of fish could easily fit the largest Amnicola into their mouths; thus, rejection of large snails (Fig. 3) was probably not due to predator gape limitation, but rather due to shell strength.

In trials with Lvm naea emarginata. a relatively weak-shelled snail, pumpkinseeds exhibited nonrandom diet preferences that varied with fish size and snail size (Fig 4; Hotelling’s T2, p<0.0001,90-mm PS; p=0.046,120-mm PS; p<0.0001,150-mm PS). When fish were given a choice of equal numbers of 3-,

6-, 9-, and 12-mm snails on sand, small fish chose smaller snails than large fish did and diet preferences of medium-sized fish were intermediate. Overall, attack and diet preference did not differ (Hotelling’s T2, p=0.22 90-mm PS; p=0.17,120-mm PS; p=0.18, 150-mm PS). But as in the previous experiment, the distribution of attack preference was skewed toward larger snails when 20

90mm PS 1..0- - 1.0 5

0. 8 - - 0.8

0.,6” - 0.6

0..4- 0.4

0. 2- + 0.2 ND 0. 0 0.0 "O 1 , 0 - 120mm PS ” 1.0 TJ ID 8 a)

% 0. 8- - 0.8 ‘aT ‘a? c 0. 6- +0.6 c o o t 0. 4- +0.4 t: o 8 o Q. CL 2 0. 2 - + 0.2 2 CL CL 0. 0 0.0 150mm PS 1. 0 - - 1.0

0, 8- - 0.8

0, 6- - 0.6

0. 4- -0.4

0, 2 - 0.2 0, 0 i 1 1 i 0.0 2 4 3 6 9 12 Amnicola Lymnaea Snail Length (mm)

Figure 3. Proportion of snails rejected by pumpkinseeds (90-, 120-, and 150-mm TL) when foraging in an arena with equal numbers of 2- and 4-mm Amnicola limosa or 3-, 6-, 9-, and 12-mm Lvmnaea emarginata on a simulated sand habitat. Vertical bars are means and 95% confidence intervals. Numbers above the bars show the numbers of fish used to calculate means. ND is no data. 21

1.0 1.0 90mm PS 0 .8 - n=7 -0.8 0 .6 - -0 .6

0.4- -0.4

0 .2 - -0.2 0 0.0 a= i— I 0.0 O L.a 0.4 ■4 -0.4 a. o •*-> o a 0.2+ -0 .2 a 0 0.0 _rj=L 0.0 150mm PS 0.8 n=7 - 0.8

0.6+ ■ 0.6

0.4 -0.4

0.2 - 0.2 0.0 <———i—1——h 1—+■ J± L 0.0 3 6 9 12 3 6 12 Snail Length (mm)

Figure 4. Attack and diet preferences of pumpkinseeds (90-, 120-, and150-mm TL) foraging in an arena with equal numbers of 3-, 6-, 9-, and 12-mm Lvmnaea emarginata on sand. Means and 95% confidence intervals (vertical bars) were based on trials using n individual fish. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference. 22 compared to preferences based on actual consumption. Presumably because of gape limitation, 90-mm pumpkinseeds rarely attacked Lvmnaea larger than

6 mm. In a few cases 90-mm fish grabbed 9- and 12-mm Lvmnaea by their foot and tore the snails’ body from the shell. All but the largest Lvmnaea could easily fit into the mouths of 120-mm pumpkinseed. Like 90-mm fish,

120-mm fish sometimes grabbed 12-mm Lvmnaea bv their foot, but they were never successful. Gape limitation was not a factor for the largest pumpkinseeds; yet they attacked more 9-mm than 12-mm L vm naea.

Nonrandom attack preferences (Hotelling’s T2, p<0.0001,90-mm PS; p=0.0005,

120-mm PS; p<0.0001; 150-mm PS) exhibited by all three size classes of fish attest to active choice by these predators. Once again, pumpkinseeds rejected more large than small snails (Fig. 3) and, in general, small fish rejected more snails of a given size class than large fish did. The only exception occurred for large fish and 3-mm snails; evidently, some large fish had difficulty manipulating small snails. This pattern occurred in the Amnicola experiment as well.

Because snail size and shell strength were correlated, I could not

determine from these experiments whether snail size or shell strength was

more important in governing pumpkinseed preference for snails. That larger

fish attacked larger snails than did small fish could easily be attributed to

either shell characteristic. Conceivably, above a given size, snails become 23 increasingly difficult to manipulate, independent of shell strength. In this regard, large fish because of their larger mouths should be able to manipulate large snails more easily than small fish would. However, I believe that rejection of snails was more likely the result of a fish’s inability to crush a snail due to its strength rather than its size. The best evidence that shell strength influenced attacks on snails comes from a comparison of the results for 90-mm fish. Whereas 90-mm pumpkinseeds readily attacked both 3- and fi-mm Lvmnaea. they almost always attacked 2-mm Amnicola to the complete exclusion of 4-mm ones. If shell strength did not influence preference for snails I would expect them to attack 4-mm Amnicola at least as readily as

2-mm ones given that they attacked 6-mm Lvm naea as much as 3-mm

Lvm naea. These kind of comparisons could not be done for the two larger size-classes of pumpkinseeds because attacks were distributed in favor of large

Amnicola.

Prev profitabilities

To explain how snail size and shell strength influenced predator choice,

I present independent data showing how these factors influenced handling times and success rate of pumpkinseed predation. Subsequently, I combine these data with estimates of prey mass to calculate profitabilities for comparison with results from selection experiments. 24 In trials in which fish were allowed to forage on only one prey type, handling times for consumed Lvmnaea emarginata increased with snail size

and decreased with fish size (Fig. 5). Handling times of medium and large fish were not influenced by the presence of alternate prey-types (Fig. 5, ANCOVA p > 0.36), i.e., handling times for Lvmnaea were similar in single- and multi-prey type experiments. For small fish, the effect of alternate prey types was significant (ANCOVA p=0.01), but I see no biological significance to such a small difference (Fig. 5). Probably because of the small size range of

Amnicola (Fig. 5), I did not detect an increase in handling time with snail size

(ANCOVA p>0.09). Likewise, the presence of alternate prey, had little effect

on handling times for Amnicola (ANCOVA. p=0.48,90-mm PS; p=0.04,120-mm

PS; p=0.13, 150-mm PS).

As estimated by regression analysis, handling times for consumed snails

increased with crushing resistance and decreased with fish size (Table 2). This

was true regardless of whether regressions were based on data from

single-prey or multiple-prey type experiments. Because crushing resistance

could not be determined for snails used in experiments, crushing resistance

was estimated from regressions of crushing resistance versus snail size based

on a sample of snails crushed at the time of experiments (Table 1). 25

30 30 □ • 90mm PS

2 0 - ■20

■ocn • □ c 120mm PS o oaj « 20 -

• • 10-

Amnicola Lymnaea Snail Length (mm)

Figure 5. Pumpkinseed handling times as a function of fish size (90-, 120-, and 150-mm TL) and snail size for snails that were consumed. Points represent means from individual fish that were allowed to forage on only one size of (closed circles) or equal numbers of different sizes of (open squares), Amnicola limosa or Lvmnaea emarginata in a sand habitat. 26 Table 2. Regressions of handling times as a function of fish size and snail crushing resistance for snails that were consumed. Ht=a+b1*C-b2,F*C, where Ht is handling time (s), F is fish length (mm), and C is crushing resistance (N). Significance levels are in parentheses.

a hi ba 3 ! n

Single nrev tvne experiments 2.71 2.11 0.01 0.49 49

(0.03) (0.0001) (0.0008)

Data from all experiments 4.04 1.58 0.007 0.54 213

(0 .0001) (0.0001) (0.0001) 27 Handling times for rejected snails were not strongly influenced by snail mass, shell strength, or fish size (Fig. 6, Table 3). On average, in single-prey experiments, pumpkinseeds handled snails for about 3 s, then either discarded them or continued shell crushing. Similarly, handling times for rejected snails were about 4 s in experiments with more than one prey type present (i.e., results using data from all the experiments in this study, Table 3). In multi-prey type experiments, handling times were correlated with snail mass but the effect of size was small. Snail length-mass regressions (Table 4) were used to estimate snail mass for regressions in Table 3.

Previously I showed that the probability that a snail would be crushed or rejected was also a function of fish size and snail size, and this was true when fish were allowed to forage on a single prey type as well. But more importantly, the probability that a snail was rejected was a function of

crushing resistance (Fig. 7). Small fish rejected a greater proportion of snails

of a given shell strength than large fish did; above a given shell strength all

snails were rejected by fish of a given size. Model parameters were estimated

with an iteratively reweighted least squares analysis using NLIN (SAS

Institute Inc. 1988). Crushing resistance was estimated from snail

size-crushing resistance equations. 28

10 10 90mm PS □ 8-- 8

6 - • □ Ej ° 6 a.*i. 4 - • • • a 4 « 2 - eg 2 • I □ w 0 0 (0 T) T3 C 120mna PS C o o o 8-- 8 o (U : □ (D « □ <0 6 - B 6 d) B a

ai B | • CT> □ C c • 2 - ! 9 2 c C

o — i—i— i—i—■—i—>—i—i— • □ m □□ □ m o X 0 < <— i— i— •— i— i— i— i— — 0 X --- 150mm PS . • • • • □ --- 8-- 1 8 --- . ODD Cl □ □ do --- 1 6 - □ amoi 6 • --- □ - --- 1 4-- □ • • 4 --- .

□ cn □ --- \ 2 mm n m—i ■'■nrr r 2 ------o n 0 ---- 0 2 4 3 6 9 12 Amnicola Lymnaea Snail Length (mm)

Figure 6. Handling times for rejected snails as a function of pumpkinseed size (90-, 120-, and 150-mm TL), snail size, and snail species. Points represent means from individual fish that were allowed to forage on only one size of (closed circles) or equal numbers of different sizes of (open squares) Amnicola limosa or Lymnaea emarginata in a sand habitat. Table 3. ANOVA tables for regressions of handling times, of rejected snails, on snail mass, crushing resistance, and fish length. SS= partial sums of squares.

Sinerle-nrev experiment

Source df SS F P Fish size (F) 1 0.63 0.17 0.68 Snail mass (S) 1 3.78 1.03 0.31 Crushing resistance (C) 1 1.78 0.49 0.49 F x S 1 1.52 0.42 0.52 F x C 1 1.71 0.47 0.50 S x C 1 4.02 1.10 0.30 F x S x C 1 2.16 0.59 0.45 Error 84 307.20

Multi-prev type experiments

Source df SS F P Fish size (F) 1 0.21 0.05 0.83 Snail mass (S) 1 18.25 4.25 0.04 Crushing resistance (C) 1 11.91 2.77 0.10 F x S 1 13.32 3.10 0.08 F x C 1 9.51 2.21 0.14 S x C 1 7.18 1.67 0.20 F x S x C 1 5.76 1.34 0.25 Error 198 850.62 Table 4. Regressions of snail mass (dry mass excluding the shell) versus snail length for Amnicola limosa. Lvmnaea emarginata. Helisoma anceps. and Phvsa sp. M=aLb, where M=snail mass (g) and L=snail length (mm).

Soecies a b r2 n

Amnicola 0.000117 1.669 0.57 8

Lvmnaea 0.000025 2.507 0.99 22

Helisoma 0.000030 2.689 0.97 10

Physa 0.000049 2.209 0.98 24 31

0.61------.. V 90mm PS „ „ A 120mm PS °*4 ” □ 150mm PS

0.2 - < z> 9 co 0.0 O'UJ

— 0.2 --

- 0 .4 -

- 0.6 Q 90mm PS i—LU o 120mm PS LU”3 150mm PS QCLU 0 .8 - o z £ 0 .6 - |_i_ o ^ 0 .4 - □ CP £ 0 .2 - o O' a. 0.0 4- 0 5 10 15 20 25 ESTIMATED CRUSHING RESISTANCE (Newtons)

Figure 7. Plot of predictions and residuals from a regression of crushing resistance and fish size on the probability that a snail would be rejected. P = 1 / [ 1 + exp (a - bj • CR + b2 • CR • FL) ] where P is the probability that a snail will be rejected, CR is snail crushing resistance, and FL is fish length. Estimated parameters, a = 3.17, bx = 1.13, and b2 = 0.006, all differed from zero (p < 0.05). 32 Profitabilities were calculated using the independently derived handling time-crushing resistance regression, probability of being rejected versus crushing resistance regression, and snail mass-length regressions. Crushing resistance was estimated from regressions based on snails crushed nearest the time of experiments (Table 1). This seemed appropriate given that crushing resistance varied somewhat during the course of this study.

In experiments, profitability alone was not a good predictor of preference. For example, 90-mm fish almost always attacked 2-mm Amnicola even though 4-mm Amnicola were more profitable (see Fig. 2). However, medium and large fish did choose the most profitable Amnicola.

Inconsistencies between attack preferences and profitabilities occurred for

Lvmnaea experiments as well (see Fig. 3). For example, 150-mm fish attacked more 9-mm snails than 12-mm ones; yet 12-mm Lvmnaea were at least as profitable as 9-mm ones. And, 120-mm fish attacked more 9- than 3-mm snails even though their estimated profitabilities were similar. Only 90-mm fish attacked Lvmnaea in a fashion consistent with estimated profitabilities. 33 Multispecies experiments- equal sizes and densities

To further test how shell strength influenced prey choice, I allowed pumpkinseeds to choose among equal numbers of different snail species of a similar size. Because shell strength differed across species, I could examine the role of shell strength while controlling for size. When given a choice of equal numbers of 6-mm Lvmnaea emarginata. Helisoma anceps. and

Campeloma decisum. attack preferences differed from random (Fig. 8,

Hotelling’s T2, p=0.001,120-mm PS; p<0.0001,150-mm PS). Both size classes offish almost certainly chose against Campeloma because of its shell strength.

When prefeeding fish, I found that only large fish crushed Campeloma and this

occurred rarely. Attack preferences, however, could not conclusively be attributed to shell strength. Although attack preference by 120-mm pumpkinseed was inversely related to shell strength, i.e., preference for

Lvmnaea (9 newtons) > Helisoma (11 newtons) > Campeloma (29 newtons), large pumpkinseeds selected against the strong-shelled Campeloma: yet, unlike

120-mm fish, large pumpkinseeds chose Helisoma over Lvmnaea. Initially I

thought I controlled for size, but in hindsight I realized that due to differences

in shell shape, 6-mm Helisoma with planispiral shells probably provided a

greater visual stimulus than 6-mm Lvmnaea or Campeloma. both with

conispiral shells. It is difficult to know how fish perceive size but to me

Helisoma appeared somewhat larger than Lvmnaea or Campeloma. Also, 34

1.0- 1.0 120mm PS 0 .8- n=7 + 0.8

0.6- ■ 0.6

a, 0.4- ■0.4 0 CD O c 1 0.2+ ■ 0.2 a) <+- L. 0.0- • 0.0 CL o 0.8 - n=8 ■ 0.8 a> Q

0.6- • 0.6

0 .4- -0.4

0.2- ■ 0.2

0.0- —i- --h- ■ 0.0 o D D o o o CD

Figure 8. Attack and diet preferences by 120- and 150-mm pumpkinseeds foraging in an arena with equal numbers of 6-mm Lvm naea emarginata. Helisoma anceps. and Campelomadecisnm. Vertical bars represent means and 95% confidence intervals; n is the number of fish used in calculations. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference. 35 based on their biomass, excluding the shell, Helisoma (3.7-5.5 mg) were larger than Lvmnaea (2.6-3.6 mg dry mass). Thus, regardless of how predators determine prey size, it is unlikely that the experiment adequately controlled for size.

Attacks in these experiments were also inconsistent with estimated profitabilities. I could not calculate profitabilities for Camneloma because I did not measure its mass, but I did calculate them for Lvmnaea and Helisoma.

Medium-sized fish chose the less profitable Lvmnaea over the more profitable

Helisoma (Fig. 8). For 150-mm fish, attacks were consistent with profitabilities.

To provide a better test of shell strength I gave pumpkinseeds a choice of equal numbers of 4.5-mm Helisoma ancens. and 6-mm Phvsa sp., Lvmnaea emarsrinata. and Helisoma ancens. Both Phvsa and Lvm naea have conispiral shells in contrast to Helisoma’s planispiral one. Shell strength from weakest to strongest was Phvsa (4 newtons) > 4.5-mm Helisoma (7 newtons) > Lvmnaea

(9 newtons) > 6-mm Helisoma (11 newtons). And, biomass estimated from

regressions was Phvsa 2.6-3.6 mg, Lvmnaea 2.2-3.2 mg, 4.5-mm Helisoma

1.9-2.9 mg, and 6-mm Helisoma 3.7-5.5 mg dry mass. Here I felt that I

controlled for size differences; 6-mm Helisoma were included for comparisons 36 with the previous experiments. To me size differences among Phvsa. Lvmnaea. and 4.5-mm Helisoma appeared small.

Overall, attacks in this experiment were more evenly distributed when compared to the previous experiments (Fig. 9). Attack preferences for small fish were non-random (Hotelling’s T2, p=0.007), but attack preferences for

120-mm fish did not differ from random (Hotelling’s T2, p=0.16).

Non-significant results may be partially due to the low replication, but also to the greater variation among individual fish rather than random selection by all individuals. In this experiment individual fish tended to cue in on one prey type, but this was not consistent among individuals within a given fish size

class. Low replication prevented tests for large fish. Pumpkinseed choice based on diet was nonrandom for small fish (Hotelling’s T2, p=0.01), but tests

were not significant for medium-sized fish (Hotelling’s T2, p=0.18). Whereas

90- and 120-mm pumpkinseed tended to choose more weak-shelled snails over

strong-shelled ones, 150-mm fish showed just the opposite trends. Results for

large fish were confounded by low replication but were consistent with results

from experiments with 6-mm Lvmnaea. Helisoma. and Campeloma. i.e.,

150-mm pumpkinseed sunfish choose 6-mm Helisoma over 6-mm Lvmnaea.

As in previous experiments these results suggest that shell strength was most

important for small and medium-sized fish and, except at extreme shell

strengths, large fish choose the largest snails available. Because the 37

1. 0 - ■ 1.0 90mm PS 0.8- n=5 • 0.8

0.6' • 0.6

0.4 - ■0.4

0.2 ■ - 0.2 0 0.0 —t— • 0.0 oV 120mm PS 0 . 8 - ■0.8 8 c n=5 c u!B v <4- 0.6- ■0.6 gj SB 2 CL +0.4 a. ■X 0.4- OG •MQ) 0.2 + + 0.2 5

0.0 ■ 0.0 150mm PS 0.8 n=2 • 0.8

0.6 - 0.6 0.4+ •0.4

0.2 ■ 0.2

0 . 0 - • 0.0 o a a a a a a a M > a E E >> o E o sz c o o -C c o m CL w CO CL .M 13 E 13 13 E 15 x E ir X X E ir x E E E CD E E E CO E E E E E E E E in

Figure 9. Attack and diet preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging in an arena with equal numbers of 4.5-mm Helisoma ancens and 6-mm Phvsa sp., Lvmnaea emareinata. and Helisoma ancens. Trials were done in a sand habitat. Means and 95% confidence intervals (vertical bars) were based on trials using n individual fish. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference. 38 proportion of snails rejected increased with shell strength, attack preferences differed from diet preferences.

Once again, there were no consistent trends between profitabilities and attack preferences (Fig. 9). The large confidence intervals preclude making inferences about the relationship between the two. Because profitabilities were very similaracross prey types for 120- and 150-mm fish, errors associated with estimating profitability parameters from regressions are likely to influence prey type rankings.

Multisnecies experiments - varying sizes and densities

In nature, prey are rarely available in equal numbers, and in fact are

usually dominated by a few species. The most desirable prey tend to be the rarest and this is no exception in the pumpkinseed-snail interaction. In

northern Wisconsin lakes Amnicola limosa and others in the family

Amnicolidae can account for more than 95% of the total number of snails

whereas large weak-shelled snails such as Phvsa. Lvmnaea. and Helisoma are

far less common (unpublished data from 21 lakes, Klosiewski and Stein). Thus

in lakes, pumpkinseeds are presented with a situation much different than

that presented to them in the previous experiments. To determine how prey

abundance may alter pumpkinseed preferences I gave fish a choice of 3-nun 39 Amnicola and 3-, 6-, or 9>mm Lvmnaea and varied the densities such that the ratios of Amnicola:Lvmnaea were 50:50, 75:25, 90:10, and 270:10.

As expected, attack and diet preferences varied with fish size, snail size, and snail species, but unexpectedly, snail choice varied little even when snail densities were skewed heavily in favor of one prey type (Fig. 10). When presented with equal numbers of similar-sized snails (3-mm Amnicola and

Lvmnaea). small fish chose the weak-shelled Lvmnaea over strong-shelled

Amnlcnlft (t-test, p=0.001); large fish showed just the opposite preferences

(t-test, p=0.04); preferences of medium fish were intermediate and did not

differ from random (t-test, p=0.7). On average medium fish attacked both

species equally, but as evidenced by the large confidence intervals, some

individuals chose Amnicola over Lvmnaea and vice-versa. Although these

snails were similar in size, large fish chose the largest snails (as in previous

experiments), even though these differences were small, i.e., Amnicola (0.9 mg)

> Lvmnaea (0.6 mg). Attack preferences of all sizes of fish agreed well with

estimated profitabilities. The relative abundance of snail types had little effect

on preferences, based on either attacks or diets, regardless of fish size or snail

size (Table 5). Even when 3-mm Amnicola were 27 times more abundant than

3-mm Lvmnaea. 90-mm fish still focused most of their attacks on Lvmnaea.

Clearly the predator was actively choosing snails, rather than sampling snails

and rejecting those with strong shells. Likewise, large and medium fish did 40

90mm PS — 3mm:3mm 120mm PS - 3mm:6mm 1.0 rfi ■■1.0 A t * 0.8 ■0.8

0.6 ■0.6

0.4 ■0.4

0.2 •0.2

0.0 it jh JQ i t _cb. jii jti --0.0 120mm PS — 3mm:3mm 150mm PS - 3mm:6mm 1.0 nh r*i r*i -■1.0 0.8 ■0.8 o ou co £ k_u -2 0.6 -0.6 £ £ P Q. 0. -* 0.4 •0.4 •X o u 0.2 ■0.2 5

0.0 -£±1 -•0.0 150mm PS — 3mm:3mm 150mm PS - 3mm:9mm 1.0 --1.0 * 0.8 •0.8

0.6 -■0.6

0.4 --0.4

0.2 •0.2

0.0 III i t -■0.0 50:50 75:25 90:10 270:10 50:50 75:25 90:10 270:10 Ratio of Amnicola:Lymnaea

Figure 10. Attack and diet preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging on varying densities and sizes of Amninnlfl limosa and Lvmnaefl emareinata in a sand habitat. Vertical bars represent means and 95% confidence intervals. Numbers above each set of bars represent the number of fish used to calculate means. Profitabilities were standardized to sum to unity and plotted as dashed lines on the same scale as preference. 41 Table 5. ANOVA results testing the effect of relative abundance of 3-mm Amnicola limosa and 3-, 6-, or 9-mm Lvmnaea emareinata on attack and diet preferences for 90-, 120-, and 150-mm pumpkinseed sunfish. For each comparison, the ratio of Amnicola.’Lvmnaea was 50:50, 75:25, 90:10, and 270:10.

£______Fish size (mm) Lvmnaea size(mm) Attack Preference Diet Preference

90 3 0.70 0.53

120 3 0.72 0.16

150 3 0.13 0.13

120 6 0.19 0.64

150 6 0.60 0.50

150 9 0.03 0.52 42 not modify their preferences even with such skewed distributions. Because fish were exposed to density treatments following a Latin Square design, the lack of any effect of relative densities was not an artifact of experimental protocol, i.e., not the result of all fish being exposed to the same densities in the same order.

Preferences switched in favor of Lvmnaea when their size was increased relative to that of Amnicola (Fig. 10). Medium and large fish almost exclusively attacked and consumed Lvmnaea regardless of its abundance relative to that of Amnicnla (Table 5). At equal densities preference for

Lvmnaea over Amnicola was not unexpected, but that these preferences did not change even when densities were skewed in favor of the non-preferred snail

270:10 was unexpected and may have important ramifications for the field.

Conceivably, because of their strong preferences for snails with specific shell characteristics, pumpkinseeds may be a major structuring force, even when predator densities are low.

Effect of habitat structural complexity

Unexpectedly, increasing habitat structural complexity did not modify pumpkinseed preferences for snails. Attack preferences of small and large pumpkinseeds for 2- and 4-mm Amnicola limosa in a simulated macrophyte 43 habitat, i.e., plastic rope at 459 stems/m2, did not differ from those in sand habitats (Fig. 11, ANOVA, p=0.29, 90-mm PS; p=0.43,150-mm PS). However, medium fish chose more 4-mm Amnicola in the artificial macrophyte habitat than in the sand habitat. Likewise, nonsignificant differences occurred when pumpkinseeds were given a choice of equal numbers of four size classes of

Lvmnaea emareinata in the same simulated macrophyte habitat when compared to results from the sand habitat (Fig. 12, MANOVA, p=0.69, 90-mm

PS; p=0.46, 120-mm PS; p=0.74, 150-mm PS).

Because plastic rope itself is not as structurally complex as most aquatic macrophytes, I also did experiments using Megalodonta beckii (225 stems/m2).

Generally, pumpkinseed preference for Lvmnaea did not differ from those in sand (Fig. 12, MANOVA, p=0.74, 120-mm PS; p=0.09, 150-mm PS). However,

90-mm fish showed some departure from results in the sand habitat. Small fish attacked more large snails than in sand and artificial macrophyte habitats.

This occurred because the structure of Megalodonta beckii allowed fish to attack the snails’ foot from below and thus gave the fish an opportunity to consume snails that they could not normally fit into their mouths.

Results from cobble habitats are difficult to interpret because snails showed some antipredatory behavior. At the start of a trial, snails were evenly distributed on rock surfaces; as the trial progressed, snails crawled among rock 44

— 90mm PS

120mm PS 0 o c 0L. M—0 0

o o

150mm PS

2 4 Snail Length (mm)

Figure 11. Comparison of attack preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging in an arena containing equal numbers of 2- and 4-mm Amnicola limosa in sand and simulated macrophyte habitats. Vertical bars represent means and 95% confidence intervals. Attack preferences in sand habitats were presented previously in Fig. 2. 45

1.0y 90mm PS IZ2Sand ■■Plastic Rope 0.8- ESIMacrophytes □□Cobble 0.6-

0.4-

0.2-

0.0-- i t _____

0.6"

0.4-

0.2-

0.0- I 12

Snail Length (mm)

Figure 12. Comparison of attack preferences of pumpkinseeds (90-, 120-, and 150-mm TL) foraging in an arena containing equal numbers of 3-, 6-, 9-, and 12-mm Lvmnaea emareinata in sand, artificial macrophyte, macrophyte, and cobble habitats. Vertical bars represent means and 95% confidence intervals. Attack preferences for the sand habitat were presented previously in Fig. 4. 46 crevices and became inaccessible to pumpkinseeds. Additionally, rocks provided snails an attachment surface which made it difficult for the fish to dislodge them. Large variances associated with these experiments were at least in part attributable to snail, rather than fish, behavior. 47 Discussion

The role of size

Size of both predator and prey played an important role in the pumpkinseed-snail interaction. Commensurate with results from other experiments using a variety of predators and prey (Mittelbach 1981, Unger and

Lewis 1983, Werner et al. 1983, Bence and Murdoch 1986), large pumpkinseeds chose larger snails than small fish did. Other shell crushing predators such as (Slootweg 1987) and wrasses (Wainwright 1988) do likewise. In contrast, redear sunfish, another shell-crushing molluscivore, show preferences independent of snail size (Stein et al. 1984). However, low replication by Stein et al. (1984), i.e., too few fish used in experiments, may account for some of this discrepancy. In other studies using pumpkinseeds, large fish also consumed larger prey than did small pumpkinseeds

(Sadzikowski and Wallace 1976, Osenberg and Mittelbach 1989).

Certainly, gape limitation contributes to size selection by predators

(Zaret 1980, Unger and Lewis 1983). Osenberg and Mittelbach (1989) show how gape limitation scales with fish size, thus limiting the sizes of snails susceptible to predation by pumpkinseeds. Consistent with their results, large pumpkinseeds in my experiments were capable of consuming larger prey than

small fish consume. But, in my experiments gape limitation was only a factor 48 when using Lvmnaea emareinata. And here, some small fish changed their foraging tactics, i.e., from a shell crusher to an oral sheller, ala Slootweg

(1987), thus enabling fish to consume snails much larger than those imposed by shell size-mouth gape constraints. Shell-crushing cichlids use this alternate foraging mode as well (Slootweg 1987). Overall, shell crushing was the predominant pumpkinseed foraging mode.

In my experiments, shell strength, more than snail size, restricted pumpkinseed diets. I found that small fish rejected a greater proportion of snails of a given size than large fish rejected, and that among species, rejection was inversely related to crushing resistance. Osenberg and Mittelbach (1989) found a similar relationship for pumpkinseeds, as did Slootweg (1987) for molluscivorous cichlids. Based on work with wrasses (Wainwright 1988), I would expect that large pumpkinseeds would generate greater forces in the pharyngeal area than small fish and thus crush stronger shelled snails. Given this relationship I would expect the shell strength constraint to be more important for small than for large fish. This pattern occurred in my experiments. The inability of a shell crusher to crush attacked prey contributes to foraging costs.

Time spent handling prey that are consumed has long been used as a measure of costs to the predator. In this regard, handling time generally 49 increases with prey size but decreases with fish size (Laughlin and Werner

1980, Mittelbach 1981, Werner et al. 1983, Bence and Murdoch 1986).

Molluscivorous redear sunfish (Stein et al. 1984) and cichlids (Slootweg 1987) follow this same pattern. In fact, consistent with my results, handling times of redear sunfish increase with snail crushing resistance (Stein et al. 1984).

Yet, Osenberg and Mittelbach (1989) contend that pumpkinseed handling times are not influenced by snail size. In their experiments, where handling times were measured on snails presented singly, handling time increased with snail size. However, when fish were allowed to forage when more than one prey was present, the relation between handling time and snail size broke down. They argue that without other prey, pumpkinseeds handle snails until they are crushed, given they do not have alternative prey. I believe these results are aberrant and more likely due to poor replication (i.e., only two fish were used in their single snail experiments). Further, I suggest that even after a pumpkinseed crushes a snail, they must spend time manipulating the snail and spitting out the shell, and that consistent with experiments using other prey, handling time should increase with prey size independent of the influence

of shell strength. For example, using mayfly nymphs, Laughlin and Werner

(1980) found that handling times were a function prey size and pumpkinseed

size. Whether my fish were exposed to multiple prey types or only one prey

type, handling times increased with snail size and decreased with fish size.

My results are consistent with those found for other molluscivores as well (see 50 Stein et al. 1984, Mittelbach 1984, Slootweg 1987). That Osenberg and

Mittelbach (1989) found no effect of fish size on handling times also could be attributed to low replication. They used a total of nine fish ranging in size from 100 to 119 mm SL; my results are based on 72 individual fish ranging in size from 90 to 150 nun TL. In my experiments handling times for rejected snails were constant across fish size and snail size and in agreement with

Osenberg and Mittelbach (1989). It may be that after a given time pumpkinseeds use some cue, such as partial shell fracturing, to decide whether to continue crushing the snail or spit it out.

Relation to models of prev selection

The strong non-random selection by pumpkinseeds, and the wide range of experimental conditions allows us to qualitatively test predictions of prey selection models. In the past, the correlation between prey size, encounter rates, and profitabilities made it difficult to distinguish among various model predictions. Under some conditions, determining active versus passive choice is difficult for the above reasons (Bence and Murdoch 1986). But, because of costs associated with shell strength, I could generate divergent model predictions and test them qualitatively. 51 Two models, the reactive field volume model (RFVM, Werner and Hall

1974) and the apparent size model (ASM, O’Brien et al. 1976), are indistinguishable under a certain set of assumptions (Wetterer and Bishop

1985) even though RFVM predicts that prey are selected as encountered whereas ASM predicts that predators choose the apparently largest prey.

When the visual field is truncated, e.g., when pumpkinseeds forage on snails in sand habitat, the models diverge (Wetterer and Bishop 1985, Chapter II).

Under these conditions, RFVM predicts that preference is frequency and density independent, whereas ASM predicts that preference is frequency and density dependent.

Pumpkinseeds clearly exhibited preferences that disagreed with both

RFVM and ASM. That attack preference varied with fish size demonstrates

active choice by pumpkinseeds and by itself refutes the premise of RFVM.

This result holds even when gape limitation was not a factor. O’Brien et al.

(1976) suggests that the RFVM is inappropriate when prey are encountered

simultaneously. Undoubtedly this occurred in my experiments. Neither model

predicts a fish size effect other than that related to visual acuity and fish size.

Further evidence against RFVM and ASM comes from experiments in which

snail sizes and numbers were similar but pumpkinseed preference was strong;

in this case RFVM and ASM would predict no selection. In another test where

prey size was decoupled from profitabilities, mosquitofish selected against the 52 largest prey, and prey choice varied with fish size (Bence and Murdoch 1986).

ASM predicts a change in diet with relative prey density yet pumpkinseed preference did not change as the ratio of AmnicolarLvmnaea was varied nor did it change with absolute density. Some may contend that given the costs associated with handling and rejecting snails, one would not expect to find agreement between pumpkinseed choice and RFVM or ASM. To the contrary,

I would argue that it is precisely these differential costs which allow us to test these models.

RFVM predicts passive choice as a function of encounters and Osenberg and Mittelbach (1989) found that encounter rates explained more than 53% of the variation in pumpkinseed selectivities for snails in the field. However, care must be taken when interpreting these results. In addition to the lack of independence when calculating their measure of variation, "I", the variation explained is highly dependent on the range of sizes of pumpkinseeds and snails used in the calculations. In this regard, I feel that the range of conditions used in my experiments provides for a more robust test of the importance of encounters. For example, if I calculated "I" for my experiments using 150-mm fish, encounter rate would explain a large proportion of the variation in attack preferences. Conversely, "I" for 90-mm fish would be poor, and in fact less than zero, suggesting that random selection was a better predictor of attack preference than encounter rate. Thus, although Osenberg and Mittelbach 53 (1989) found snail choice correlated with estimated encounter rates in the field, the strength of any causal relationship between encounters and snail choice becomes suspect given my results.

Predictions using an optimal foraging construct are inconsistent with my results as well. Pumpkinseeds did not always include the most profitable prey in their diets as predicted by optimal foraging models (Werner and Hall 1974,

Chamov 1976, Mittelbach 1981). Instead, pumpkinseeds showed humped-shaped preferences even though the densities of prey types were equal. Variable attack probabilities should contribute to humped-shaped preference curves (Osenberg and Mittelbach 1989). But Osenberg and

Mittelbachs’ optimal foraging model explains little of the residual variation for pumpkinseeds feeding on snails when compared to models based on encounter rates and/or capture probabilities. Previously, humped-shaped preference curves have been attributed to mistakes by the predator and simultaneous encounters with prey. The small confidence intervals in most of my experiments rules out predator mistakes. Predictions from an optimal foraging model that includes capture probabilities and allows for simultaneous encounters agrees with my results (Chapter II), but this model tests only the

simple two-prey type experiments. Thus, although not robust, some support

exists for energy maximization by pumpkinseeds. Implications for the field

Given my results, I would expect pumpkinseeds to play a major role in structuring snail assemblages in lakes. In a variety of aquatic systems, selective predators have influenced prey size and species composition (Crowder and Cooper, Kerfoot and Sih 1987, Morin 1984, Sih et al. 1985). Gape limited predators such as the mummichog affect snail abundance and size distribution

(Joyce and Weisberg 1986); shell crushers, such as crabs, are an important source of mortality for marine molluscs (Hitching et al. 1966, Vermeij 1978,

1979, Palmer 1979). Freshwater snails do not possess the thick shells and ornamentation exhibited by their marine counterparts (Vermeij and Covich

1978), but nevertheless can reduce their risk of predation through shell

strength or by growing large relative to predator gape size. In lakes with pumpkinseeds I would expect to find snail assemblages dominated by small

and/or thick-shelled species. Given that pumpkinseeds strongly preferred

snails with specific characteristics even when abundances were highly skewed in favor of another species, it may take few predators to affect snail

assemblage structure. The size structure of the predator population also may

influence snail species composition because small pumpkinseeds were more

constrained by snail size and shell strength than were large fish. A small

thick-shelled snail should be least prone to all sizes of predators because small

fish select against strong-shelled snails and large pumpkinseeds select for 55 large snails. This may explain the predominance by small, strong-shelled snails in northern Wisconsin lakes (Klosiewski and Stein, unpublished data). 56 Literature Cited

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Osenberg, C. W., and G. G. Mittelbach. 1989. Effects of body size on the predator-prey interaction between pumpkinseed sunfish and gastropods. Ecological Monographs 59:405-432.

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Sadzikowski, M. R., and D. C. Wallace. 1976. A comparison of the food habits of size classes of three sunfishes (Lenomis macrochirus Rafinesque, L. gibbosus (Linnaeus), and L. cvanellus Rafinesque). American Midland Naturalist 95:220-225.

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Stamps, J., S. Tanaka, and V. V. Krishnan. 1981. The relationship between selectivity and food abundance in a juvenile lizard. Ecology 62:1079-1092.

Slootweg, R. 1987. Prey selection by molluscivorous cichlids foraging on a schistosomiasis vector snail, Biomphalaria glabrata. Oecologia 74:193-202.

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Vermeij, G. J. 1979. Shell architecture and causes of death of micronesian reef snails. Evolution 33:686-696.

Vermeij, G. J., and A. P. Covich. 1978. Coevolution of freshwater gastropods and their predators. The American Naturalist 112:833-843. 59 Wainwright, P. C. 1988. Morphology and ecology: functional basis of feeding constraints in Caribbean labrid fishes. Ecology 69:635-645.

Wetterer, J. K., and C. J. Bishop. 1985. Planktivore prey selection: the reactive field volume model vs. the apparent size model. Ecology 66:457-464.

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Zaret, T. M., and J. S. Suffern. 1976. Vertical migration in zooplankton as a predator avoidance mechanism. Limnology and Oceanography 21:804-813. CHAPTER II EVALUATING AN OPTIMAL FORAGING MODEL INCORPORATING CAPTURE SUCCESS AND SIMULTANEOUS ENCOUNTERS WITH PREY

Introduction

The abundance of empirical and theoretical work on optimal diets, e.g.,

Chamov (1976), Engen and Stenseth (1984), Emlen (1966), Lacher et al.

(1982), Mittelbach (1981), Osenberg and Mittelbach (1989), Stein et al. (1984),

Waddington and Holden (1979), and Werner and Hall (1974), attests to the importance of my understanding of prey choice. Common to all work in this area is the basic assumption that by foraging optimally, a predator ultimately enhances its individual fitness. At the population and community level, predators, by selectively choosing prey, directly influence prey species

abundance and composition (Brooks and Dodson 1965). From the predators’ perspective, common to all work in this area is the basic assumption that by

foraging optimally, a predator ultimately enhances its individual fitness. Thus,

whether working at the individual, population, or community level, using

simple models to predict diets has been most alluring.

Predictions from models are, however, only as good as our ability to

satisfy model assumptions. In this regard, predictions of predator diets can be

60 61 significantly altered simply by changing the way in which prey are encountered

(Engen and Stenseth 1984, Waddington 1982). Some optimal foraging modelers (e.g., Charnov 1976, Mittelbach 1981, Werner and Hall 1974) assume that prey are encountered sequentially, i.e., one at a time, whereas others (e.g.,

Engen and Stenseth 1984, Waddington and Holden 1979) assume that prey can be encountered simultaneously. These divergent assumptions about how prey are encountered produce strikingly different predictions about partial preferences for prey and density-dependent predation.

In my work on pumpkinseed sunfish (Lepomis gibbosus) preying on snails (Chapter I), I came to realize that in my experiments, fish encountered snails simultaneously. Thus, comparing prey choice by pumpkinseed sunfish to predictions from a sequential-encounter optimal foraging model developed by Osenberg and Mittelbach (1989) for this same species seemed inappropriate.

Further, previously developed simultaneous encounter models also seemed inappropriate because not all snails attacked by pumpkinseed are consumed yet the models assume that they are. Therefore, I modified an existing

simultaneous encounter model (Waddington and Holden 1979) and compared

its predictions with results from laboratory experiments. 62 The Pumpkinseed Sunfish-Snail Interaction

I modeled pumpkinseed sunfish preying on snails over a sand substrate.

Snails were distributed on the sand surface; fish located snails while hovering above the bottom. Upon attacking a snail, pumpkinseed sunfish either consumed the snail by crushing it, spitting out the shell, and swallowing the soft body, or spit out the snail intact and thus received no benefit from attacking that snail (Chapter I). 63 The Model

The following model was derived from an optimal foraging model developed by Waddington and Holden (1979) for bees foraging on two flower types. In their model, both flower types were assumed to be encountered simultaneously. The proportion of visits to a flower type is a function of time spent travelling between flowers, handling times, and caloric rewards associated with choosing one flower type over the other. I applied similar foraging constraints to pumpkinseed sunfish regarding costs and rewards. The major differences between my approach and that of Waddington and Holden

(1979) derives from my allowance for unsuccessful captures by the predator as well as for a different geometry between predator and prey; namely, snails are distributed in a two-dimensional plane above which fish locate and pursue them (Fig. 13).

I assume, as do other optimal foraging modelers (see Pyke 1984 for a review), that predators benefit by choosing the most profitable prey. The most profitable prey maximize energy intake, E, per unit time spent foraging, T. I assume that energy intake is proportional to biomass consumed and that this proportion is constant over all prey types. Thus,E is simply prey biomass consumed. 64

Sand Substrate

Figure 13. Schematic diagram of the spatial relationship between a pumpkinseed sunfish in the water column (distributed in three dimensions) and a potential snail prey on a sand substrate (distributed in two dimensions). Pursuit distance z can be calculated as z = (X2 + y2)*. See text for a description of parameters. 65 When a predator simultaneously encounters two prey types, the predator should attack the prey type with the larger ratio of expected energetic reward per expected cost in time; that is, choose the prey type with the larger

E(E)/E(T). For pumpkinseeds feeding on snails, not every attack is successful.

To estimate E(E), both successful and unsuccessful attacks must be considered.

If the probability of successful capture in an attack on prey type i is p c , then

E(E{) = p C( -e* + (1 -pC() -0 (1) where et is the biomass of prey type i.

The time cost in capturing a prey must include time spent searching(ta), pursuing (tp = time between locating and grasping a prey), and handling (th = time spent manipulating a prey) the prey item, as well as time spent in unsuccessful attacks. Note that in other models, handling time sometimes refers the sum of my pursuit and handling times. I assume that search time is instantaneous, thus ts = 0; this seems reasonable when prey are abundant, i.e., when prey are encountered simultaneously. In this formulation, search,

pursuit, and handling are distinct acts, i.e., none of these foraging components

occurs simultaneously. Costs associated with time spent in each activity, also

are assumed to be equivalent; I make no assumptions about the energetic costs

of foraging because costs associated with handling snails are small relative to

rewards (Stein et al. 1984). For prey type i, the expected time spent per

capture is 66

E (T t) = pC{ • ( tPi + thi for prey consumed) + (1 - pCi) ( tPi + tht for prey rejected) .

Time spent in pursuit is a function of the fish’s swimming speed while pursuing prey and the distance between predator and prey. Thus,

t, =■ £ O) V where z is the distance between the predator and prey and v is the average swimming speed of the predator while pursuing prey. Ify is the distance from the predator to the substrate and X is the distance from the snail to a point directly below the predator (see Pig. 13), then tp is

, . vfr* . (4) P V I assume that the predator returns to height y while handling snails; thus, time is not added to account for the fish’s movement back to its original height.

I assume that tp is the same for rejected and consumed prey, but that

th depends on the success of an attack. ’Then becomes

E(Tt) = pCi • UP| + tkc) + (1 - Pc)(tPi + tkr) (5)

= & U +y + PCt’thc 1 ci + (1 ~ PC|)- * h rri where ^^is handling time for successful attacks on prey type i and thr

is handling time for attacked but rejected prey type i. 67 Then

E(Ej) ______P c, • e(______E(^Ti) Jxf + y 2 (6) and a predator should choose prey type 1 when

E(EX) ^ E(E2) m E(TX) > E(T2) or

rclPc e 1 ~ r Pce CS I (8) Jx* + y 2 + u ^X22 + y 2 n f f , where

- PC( • *Ae| + ( 1 - Pc, ) • **r( > » ■ 1, 2 . (9)

Solving inequality (8) for Xu we find that the predator should choose type 1 when

(10) X ,< \ [r J X* +y2 + b) - where R, the ratio of the expected reward per attack for prey types 1 and 2, is

R = Pc, e i (11) Pc, e2 and

b = v (RH2-H x) (12) 68 To forage optimally, a predator must weigh the tradeoffs associated with choosing one prey type over another. Hence, I assume that 1) the predator can, based on prey morphology, distinguish between types and 2) the predator can assess average prey biomass, mean handling times, and the probabilities of capture success per attack for each prey type. In Chapter I, I clearly show that pumpkinseed sunfish distinguish among prey types based on size and shell characteristics that influence handling times and capture success.

Whether prey type 1 is chosen over prey type 2 depends at least in part, on R, the mass of that prey type, ingested per attack, relative to the other prey type. Predators should attack large prey and prey with a high capture success rate per attack. Also, prey with short handling times {H1 vs H2) and those with high expected reward per attack,R, should be chosen over those with long handling times or low expected benefit.

The probability that inequality (10) is true, given a particular value of

X2 is

Pr (X1

(14) 69 Over all values ofX2, the probability, p*, that inequality (10) is true is

M p* - JPr(Xl

If we assume that each prey typei has an independent Poisson spatial distribution with mean density Dit the probability that at least one prey of type i is located within a circle defined by radius r is

r Pr[0

it Pr(Xx< a | X2) = j2nD 1xle-°'Kxidx1 = 1 - e'0'**' (17) 0 and from equation (15)

p* - J[ 1 - e'iW] 2nD2x2e~D,1tx*dx2 (18) where

0 , where Ry + b ^ y c = (19) , elsewhere R Expression (19) limits X2 such that a >= 0; for values of X2 such that a < 0, inequality (10) will always be false. Equation (18) must be solved numerically. 70 The probability that prey type 2 is attacked is,

q' « 1 - P ‘ • (20)

To predict diets, i.e., the relative proportion of each prey type in the predator’s stomach, attack probabilities must be discounted by the proportion of prey that are rejected once attacked. Thus, the proportion of prey type 1 in the predator’s diet is

p D* = ------(21) P*PCl + 9*Pc, and the proportion of the other prey type in the predator’s diet is

Q d ■ 1 ~Pd • (22) 71 Materials and Methods

Model solutions were calculated as a function of prey abundance.

Solutions were determined for the midpoint of three size-classes of pumpkinseed sunfish, 90-100, 120-130, and 150-165 mm, foraging on 3-mm

Amnicola limosa and Lvmnaea emarginata. Except for swimming speed, which was derived from a bluegill (Lepomis macrochirus) foraging experiment

(Mittelbach 1981), all model parameters were based on data from previous experiments (see Table 6) in which pumpkinseed sunfish were allowed to forage on 100 individuals of a given snail type over sand (Chapter I). Handling times and capture success, as measured in laboratory experiments, were estimated from regressions on snail crushing resistance and fish size.

Crushing resistance was estimated from regressions of species-specific crushing resistance on snail length. Snail mass was estimated from mass-length regressions.

The proportion of attacks on a prey type, equation(18), was solved numerically, to eight places, using Rhomberg integration (Press et al. 1988).

Attack preference (Bence and Murdoch 1986) was calculated from the predicted proportion of attacks on each prey type. Attack preference measures how attacks on prey differ from random just as diet preference (Chesson 1973) measures how inclusion of prey in diets differs from random. Given two prey 72

Table 6. Model parameters used in simulations of 90-150 mm pumpkinseed sunfish foraging on 3-mm Amnicola limosa and Lymnnefl emareinata. Estimate of swimming speed comes from Mittelbach 1981; all other parameters are from Chapter I. PS = pumpkinseed sunfish.

Parameter Descrintion Parameter Value

Swimming speed, m/min v 1.7

Fish’s distance above the bottom, cm y 10.0

Amnicola Lvmnaea Snail mass, mg e 0.9 0.6

Handling time for rejected snails, s thr 3.0 3.0

Handling time for consumed snails, s thc

90 mm PS 10.0 5.5

120 mm PS 8.1 4.8

150 mm PS 6.1 4.0

Proportion of snails consumed pc

90 mm PS 0.41 0.84

120 mm PS 0.68 0.91

150 mm PS 0.88 0.94 73 types, a preference value > 0.5 indicates preference for a prey type whereas a value < 0.5 indicates selection against a prey type.

Model predictions also were compared to results from experiments in which fish size, snail size, snail species, and the densities of snails were varied

(Chapter I). Three size classes of pumpkinseeds, i.e., 90-100, 120-130, and

150-165 mm were allowed to forage on 3-mm Amnicola limosa and Lvmnaea emareinata in a 100 x 42 x 33 cm experimental arena. The ratios of Amnicola to Lvmnaea were 50:50, 75:25, 90:10, and 270:10. Attack preference (Bence and Murdoch 1986) was calculated for experiments and model output. 74 Results

Model Simulations

Tradeoffs associated with choosing between snail types were nicely demonstrated in model simulations. Because handling time for a given prey type declines with increasing fish size and capture success increases with increasing fish size, the model predicted that attacks on, and preferences for, snails should vary with fish size (Fig. 14). Although 3-mm Amnicola (0.9 mg) weigh more than 3-mm Lvmnaea (0.6 mg), the model predicted that small fish

(90 mm) should choose Lvmnaea over Amnicola. Conversely, medium-sized

(120 mm) and large fish (150 mm) were predicted to choose Amnicola over

Lvmnaea. These differences occurred because small fish are more successful

at crushing Lvm naea than Amnicola and spend less time handling Lvmnaea than Amnicola. Although larger fish also crush Lvmnaea more easily than

Amnicola. the relative differences in handling times between snail types are

far less than for small fish. Hence, larger fish should maximize E/T by

selecting Amnicola with its greater mass.

In simulations where total snail abundance was held constant, but the

proportion ofAmnicola and Lvmnaea was varied, the model predicted partial

preferences for prey (Fig. 14). Instead of predicting that snail types are always

attacked or not attacked, or that attacks on snails are proportional to their 75

1.0 1000 1000 ■■1.0 100. - 100 0.8 ■0.8 10

0.6 1 Snail -m- 2 ■■0.6

■§ 0.4 -■0.4 O 9 0.2 ■■0.2 O 90 mm PS

0.4 •0.4 0.2 -0.2 100 0.0 1------1—•- «r-r 0.0 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 1.0 Proportion of Lymnaea in the Environment

Figure 14. Predicted proportion of 3-mm Lvm naea attacked and attack preference for Lvmnaea as a function of proportion ofLvm naea. relative to Amnicola. in the environment, pumpkinseed sunfish (PS) size, and total snail abundance. 76 abundance, the model predicted that attacks on a prey type should increase as its abundance increased in the environment, though not necessarily in proportion to its abundance in the environment. Regardless of whether a snail type was preferred or not, predicted attacks increased as its proportion increased in the environment (Fig. 14). As total abundance increased, preferences for and against snail types became stronger. However, changes in preference with increasing total snail abundance were not merely a function of increasing the abundance of the preferred type, but rather a result of changes in both absolute and relative abundance.

That the model predicts that both relative and absolute abundance influence predator choice is best illustrated in Fig. 15. Regardless of whether

Lvmnaea was the preferred or non-preferred type, the predicted proportion of

attacks on Lvmnaea decreased as Lvmnaea abundance was held constant and

Amnicola abundance increased. Similarly, predicted attacks on Lvmnaea increased as its abundance increased while Amnicola abundance remained

constant. Preference for the preferred snail type increased as its abundance

increased. But, preference for the preferred type also increased as the

abundance of the non-preferred type increased because attacks on

non-preferred snails did not increase in proportion to their increase in the

environment (Fig. 15). Figure 15. Predicted proportion of of proportion Predicted 15. Figure pumpkinseed sunfish (PS) size, and Lvmnaea abundance. of Lvmnaea and size, (PS) function sunfish a pumpkinseed as Lvmnaea for

Lymnaea as a Proportion of Snails Attacked 0.4-- 0.6 0.8 0.4- ■ 0.4- 0 0 0 . - \ V - 0.8 0.0-— < - ■ * — « 0.0 0.4- 0.2 1 1 . . . . . 0 2 0 0 2 -> --. -- 1 - - - "^siooo

100' \ 10 10 100 120 mm PS mm 120 150 mm PS mm 150 100 90 mm PS mm 90 mioam 2 Amnicola-m Amnicola Amnicola Lvm naea naea Lvm 1 1000 100 100 abundance in the environment, environment, the in abundance attacked and attack preference preference attack and attacked 10 100 1000 - ♦ *-0.0 ■■ ••0.4 ■ ■ ■ - - -0.4 ■ ■ ■ • ■0.4 ■ 0.8 0.2 0.6 0.8 0.0 0.6 0.2 0.6 0.8 0.0 0.2 1.0 1.0

Attack Preference for Lymnaea 77 78 Interestingly, situations can occur where the preferred snail type becomes the non-preferred type, and vice-versa, simply by changing total prey abundance but holding relative abundance constant (Fig. 16). For example, under a hypothetical situation where capture success for each prey type is 1.0, but th Cj =5 s, th c% =15 s, e,=l mg, and e2=2 mg, the model predicted that predators should prefer prey type 2 at low overall prey abundance, but disproportionately increase their attack rate on prey type 1 as total prey abundance increases. The model predicts that eventually, prey type 1 becomes preferred over prey type 2 simply because total abundance increased.

Comparison with results from laboratory experiments

The model did well in predicting how attacks by pumpkinseeds, in laboratory experiments, varied with fish size and prey abundance, but the agreement between predicted and observed values for attacks was not strong

(Fig. 17). As predicted by the model, the observed proportion of attacks on

Lvmnaea was greater for small than large pumpkinseeds. And, the observed proportion of attacks by small pumpkinseeds on Lvmnaea increased as

Lvmnaea abundance was increased relative to Amnicola’s. thus providing good

agreement with model predictions. Likewise, for 120- and 150-mm fish, even

though the model predicted only small increases in attacks on Lvmnaea as it

was increased in proportion to Amnicola. this trend reflects the more dramatic 79

1000

L_ 0 .8 - H-o 100

0.0 - ■a N 0.0 0.0 0.2 0.4 0.6 0.8 1.0

Proportion of Snail Type 1 in the Environment

Figure 16. Predicted proportion of attacks on and preference for a hypothetical snail type as a function of its relative abundance in the environment for the hypothetical situation where pc=1.0,th =5 s, th Pi =15 s, e2=l mg, and es=2 mg. See Table 6 for parameter descriptions. 80

1,0 9 0 m m PS X S El -- 1.0 & 9 e 1 i 1

0.8 - -- 0.8

□\ 7. - ^ OBSERVED X ± 9595 Cl 0. 6 - -- 0.6

TJ O.44. • 'MODEL PREDICTION 0 - - 0 .4 O O 0.2 + -- 0.2 -M• * -> O < 0 jn 0.0 0.0 O .. 120 mm PS c *5 1.0 -- 1.0 c E C/3 >> 0.8 -• -- 0.8

C o 0.6 ■■ 0.6 0 u c o 0.4 --0 .4 0 CL l_ o M-0 0 0.2 -- 0.2

CO 0.0 -B-f- e - a — h O. 0.0 O i l u 1.0 .. 150 mm PS -- 1.0 o O 0 o c 0.8 -- -- 0.8 E o >> in 0 .6 -- mo - - 0.6

0 .4 + --0 .4 m o o CM in t -- 0.2 0.2+ 0 o(j) i CM T • 0.0 - S - t -+-B- 1 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Proportion of Lymnaea in the Environment

Figure 17. Observed and predicted proportion of Lvmnaea attacked and attacked preference for Lvmnaea as a function of proportion of Lvmnaea in the environment and pumpkinseed sunfish (PS) size. Open squares represent model predictions and closed circles and vertical bars represent observed means and 95% confidence intervals. 81 increase in attacks on Lvmnaea with increased Lvmnaea abundance in laboratory experiments, even though Amnicola was the preferred snail.

Results from experiments also supported the model’s prediction of partial preferences for prey. In experiments, snails were not attacked in proportion to their abundance in the environment nor were snail types always attacked or excluded from the diet.

The model fared similarly in predicting how pumpkinseed attack preferences varied with prey abundance in laboratory experiments. When total

snail abundance was held constant and the ratio of Amnicola:Lvmnaea was varied 50:50,75:25, and 90:10, or when Lvmnaea abundance was held constant

at 10 but Amninnla abundance was increased from 90 to 270, observed attack

preferences remained relatively constant as predicted by the model (Fig. 17).

Agreement between predicted and observed values for attack preference,

however, varied with fish size. Small fish had highest preferences for

Lvmnaea in experiments and model simulations and provided the best

agreement between observed and predicted values for attack preference.

Conversely, the agreement between observed and predicted values for medium

and large fish was poor, though for both size classes, observed and predicted

attack preferences varied little with relative abundance of prey. 82 Discussion

My model builds on previous work in optimal foraging theory by 1) accounting for prey that are attacked but not necessarily consumed, 2) allowing predators to encounter prey types simultaneously, and 3) accounting for predators foraging in a three-dimensional space on prey distributed in a two-dimensional plane. The model predicts density-dependent prey choice as well as partial preferences by the predator. Predictions were supported, at least qualitatively, by results from laboratory experiments in which pumpkinseed sunfish foraged on snails (Chapter I). As such, the predictions provide support for, and sharp contrasts with, predictions from other optimal foraging models.

Concerning what appears to be my most important assumption, pumpkinseeds undoubtedly encountered snails simultaneously in my

experiments. The density of the rarest species was 24 m'2. If the reactive

distance of pumpkinseed sunfish to snails is the same as that to zooplankton

(Confer and Blades 1975), the reactive distance for 3-4 mm snails would be 60

cm. If pumpkinseeds have a 360° visual field and snails are randomly

distributed, the probability that at least one snail will be within the reactive

distance of a pumpkinseed at the lowest snail density approaches 1.0 (see

Appendix B). Even if I conservatively assume that the visual field is 180°, the 83 probability that at least one snail is within the reactive distance still

approaches 1.0 (see Appendix B). Clearly, using a simultaneous encounter

model for my experimental data was appropriate. Although my model was not

developed for structurally complex habitats, such as macrophytes, I might

expect that pumpkinseed encounter snails simultaneously in nature, given that

in a survey of 21 northern Wisconsin lakes (Klosiewski and Stein, unpublished

data) snail densities in macrophytes averaged 2200 m'2 and ranged as high as

6500m'2. In bare sediment habitats snail densities averaged 600 m'2 and

ranged as high as 1900m'2; thus, at least under certain conditions, support

exists for the existence of simultaneous encounters in the field.

The model predicts that unsuccessful captures influence attacks on snail

types by contributing to total time spent pursuing and handling prey per prey

item consumed. It seems reasonable to assume that preferences for snail types

varied among size classes of fish in laboratory experiments and in model

predictions because capture success varied with pumpkinseed size (Chapter I).

In other studies using sequential-encounter models, one with pumpkinseed

sunfish and snails (Osenberg and Mittelbach 1989) and the other with

Chaoborus diets (Pastorok 1981), the incorporation of capture success into the

models was important to their predictive capability. In both models, capture

success increased total handling time and thus modified profitability curves.

After Chamov (1976), my pursuit and handling time are incorporated into 84 handling time and, as such, were both influenced by capture success. Thus, regardless of whether models assume sequential or simultaneous encounters with prey, capture success, because of its effect on total handling time, ultimately influences prey choice.

In a predator-prey system with unsuccessful captures, optimal foraging

models predict attacks rather than diets. Thus, when capture success is

important, caution must be used because failure to include it in the model, or

to use it incorrectly, is just as likely to lead us to falsely accept as reject its

predictions. In Chapter I, I provide empirical evidence for the importance of

distinguishing between attack and diet preferences when prey are attacked but

not necessarily consumed. To predict diets from my model, I have shown that

the predicted proportion of attacks must be discounted by unsuccessful

captures (equation 21). Likewise, a similar adjustment must be made to

predictions from other models when there are unsuccessful captures and

results are compared to diets rather than attacks on prey, regardless of

whether the model assumes sequential or simultaneous encounters. Previous

workers (e.g., Osenberg and Mittelbach 1989, Pastorok 1981) compare their

predictions with field data, i.e., prey in stomachs, but do not provide enough

detail to determine if they actually compared diets with predicted attacks

rather than predicted diets. Herein, I did not discount predicted attacks by

unsuccessful captures; predicted attacks were compared with observed attacks. 85 For the most part, the manner in which prey are encountered seems to have the largest effect on model predictions. In my model, allowing for simultaneous encounters with prey resulted in predictions of partial preferences. This prediction was supported by the empirical results presented here as well as other experiments with pumpkinseed sunfish and more than two snail types (Chapter I). Given that my model derives from a model

(Waddington and Holden 1979) that generated partial preferences, it is not very surprising that my model also predicts partial preferences for prey. In contrast, sequential-encounter optimal foraging models (e.g., Chamov 1976,

Werner and Hall 1974, Mittelbach 1981) predict that encountered prey are either always attacked or never attacked. However, data do not always support this prediction (see e.g., Elner and Hughes (1978), Mittelbach 1981).

Both Waddington and Holden (1979) and O’Brien et al. (1976) suggest that simultaneous encounters with prey are common in nature; as such, this may well account for discrepancies between model predictions and actual diets. In a generalized version of the classical optimal foraging model, i.e., one similar to Chamov (1976) but which allows for sequential or simultaneous encounters with prey, Engen and Stenseth (1984) predict either partial or complete preferences (i.e., prey are always attacked or not attacked), depending on how prey are encountered. Similarly, by incorporating variable attack rates in their

sequential-encounter model for pumpkinseed sunfish foraging on snails,

Osenberg and Mittelbach (1989) provide support for partial preferences for 86 prey. However, to include probability of attack in a model that predicts which prey should be attacked seems circular to us. When a predator is simultaneously given a choice between two prey types that are randomly distributed it seems intuitive that partial preferences should occur simply because the type closer to the predator will vary. Non-unique profitability rankings result because pursuit times, as well as prey handling and energetic rewards, must be included in the decision process.

The spatial geometry presented herein tends to reduce differences in pursuit time between prey types. The farther a predator forages above the bottom, the less effect prey density will have on prey choice. In addition to predicting prey choice, the model also suggests that a predator should forage as close to the bottom as possible when feeding on prey distributed on the bottom.

As an additional contrast to predictions from sequential-encounter models, my simultaneous encounter model and that of Engen and Stenseth

(1984) predict that increasing overall prey abundance does not necessarily lead to a more specialized diet. In my model, increasing overall abundance results in shorter pursuit times for both prey types, which under certain conditions results in more specialized diets (e.g., see Fig. 14). Conversely, under other conditions, preferences for and against snail types can switch as total prey 87 abundance is increased due to density-dependent changes in profitabilities.

Barkan and Withiam (1989) and Stephens et al. (1986) also show how preferences for the preferred and non-preferred types may switch with changes in prey abundance. But, in their models, this switch was due to changes in encounter rates, the end result being that as a long-term strategy predators may not choose the most profitable prey, i.e., when profitability is based simply on energetic reward and handling time. In my model the switch can occur simply because of changes in profitability with changes in prey density.

Conceivably, the switch between preferred and non-preferred prey with changes in prey density could occur in these models (Barkan and Withiam

1989; Stephens et al. 1986) as well as sequential-encounter models (Chamov

1976; Mittelbach 1981; Werner and Hall 1974) simply because of a reduction in pursuit time with increasing prey density. If pursuit time is a function of the distance predators first locate prey and the speed at which predators pursue prey, it follows that, at least to some point, pursuit time should be inversely related to prey abundance. Yet in many tests of foraging models, researchers define handling time as I have, and then consider the remainder as search time. Thus, their encounter rates are really a combination of pursuit time and search time. For example, Werner and Hall (1974) explicitly stated that they expected pursuit time to be small relative to handling time, thus they

excluded pursuit time from their model. However, a reactive distance of 60 cm 88 (O’Brien et al. 1976) coupled with pursuit speeds of 28 cm's'1 (Mittelbach

1981) yields a pursuit time of nearly 2 s, almost twice that of the single-second handling times measured by Werner and Hall (1974). Thus, Werner and Hall

(1974) and others may have erroneously measured "encounter rates" and

"search times". Whereas, one cannot assess how these errors might modify model predictions, it is clear that simultaneous encounters coupled with unsuccessful captures yield quite different predictions from sequential models.

Evaluating these features of any predator-prey interaction should provide more realistic models and a better understanding of predator behavior. 89 Summary

My model accounts for unsuccessful captures and simultaneous encounters with prey. Unsuccessful captures influence attacks on snails by contributing to time spent handling and pursuing prey. Allowing for simultaneous encounters with prey resulted in partial preferences for prey because of non-unique profitability rankings. Rather than predicting that a more specialized diet would result from increasing the overall abundance of prey, the model predicted that non-preferred prey could actually become preferred simply because of an overall change in prey abundance. Finally, the model predicts that attacks on a prey type should increase as its abundance increases regardless of whether it is the preferred or non-preferred type.

Laboratory experiments with two prey types, which differed in handling, capture success, and biomass, quantitatively supported predictions for small predators and qualitatively supported them for medium and large predators.

Clearly, assessing how predators encounter (either sequentially or simultaneously) and capture prey (either all attacks are successful or not) will contribute to our understanding of predator diet selection. 90 Literature Cited

Barkan, C.P.L., and M.L. Withiam. 1989. Profitability, rate maximization, and reward delay: a test of the simultaneous-encounter model of prey choice with Parus atricanillus. The American Naturalist 134:254-272.

Bence, J.R., and W.W. Murdoch. 1986. Prey size selection by the mosquitofish: relation to optimal diet theory. Ecology 67:324-336.

Brooks, J.L., and S.I. Dodson. 1965. Predation, body size, and composition of plankton. Science 150:28-35.

Chamov, E.L. 1976. Optimal foraging: attack strategy of a mantid. The American Naturalist 110:141-151.

Confer, J.L., and P.I. Blades. 1975. Omnivorous zooplankton and planktivorous fish. Limnology and Oceanography 20:571-579.

Elner, R.W., and R.N. Hughes. 1978.. Energy maximization in the diet of the shore crab, Carcinus maenas. Journal of Animal Ecology 47:103-116.

Emlen, J.M. 1966. The role of time and energy in food preference. The American Naturalist 100:611-617.

Engen, S., and N.C. Stenseth. 1984. A general version of optimal foraging theory: the effect of simultaneous encounters. Theoretical Population Biology 26:192-204.

Lacher, T.E., Jr., M.R. Willig, and M.A. Mares. 1982. Food preference as a function of resource abundance with multiple prey types: an experimental analysis of optimal foraging theory. The American Naturalist 120:297-316.

Mittelbach, G.G. 1981. Foraging efficiency and body size: a study of optimal diet and habitat use by bluegills. Ecology 62:1370-1386.

O’Brien, W.J., N.A. Slade, and G.L. Vinyard. 1976. Apparent size as the determinant of prey selection by bluegill sunfish (Lepomis macrochirus). Ecology 57:1304-1310.

Osenberg, C.W., and G.G. Mittelbach. 1989. Effects of body size on the predator-prey interaction between pumpkinseed sunfish and gastropods. Ecological Monographs 59:405-432. 91 Palmer, R.A. 1981. Predator errors, foraging in unpredictable environments and risk: the consequences of prey variation in handling time versus net energy. The American Naturalist 118:908-915.

Pastorok, R.A. 1981. Prey vulnerability and size selection by Chaoborus larvae. Ecology 62:1311-1324.

Press, W.M., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling. 1988. Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge, U.K. 768 pages.

Pyke, G.H. 1984. Optimal foraging theory: a critical review. Annual Review of Ecology and Systematics 15:523-575.

Stein, R.A., C.G. Goodman, and E.A. Marschall. 1984. Using time and energetic measures of cost in estimating prey value for fish predators. Ecology 65:702-715.

Stephens, D.W., J.F. Lynch, A.E. Sorensen, and C. Gordon. 1986. Preference and profitability: theory and experiment. The American Naturalist 127:533-553.

Waddington, K.D. 1982. Optimal diet theory: sequential and simultaneous encounter models. Oikos 39:278-280.

Waddington, K.D., and L.R. Holden. 1979. Optimal foraging: on flower selection by bees. The American Naturalist 114:179-196.

Werner, E.E., and D.J. Hall. 1974. Optimal foraging and the size selection of prey by the bluegill sunfish (Lepomis macrochirus). Ecology 55:1042-1052. CHAPTER III FISH PREDATION AND ITS ROLE IN STRUCTURING SNAIL ASSEMBLAGES IN NORTHERN WISCONSIN LAKES

Introduction

Predators are a major force in structuring communities (see Sih et al.

1985 for review). Evidence supporting the importance of predation comes from terrestrial (Harper 1969, McNaughton 1979, 1983), marine (Connell 1961,

1975, Paine 1966, 1974, 1980), and freshwater systems (Brooks and Dodson

1965, Wells 1970). In freshwater, most support for the importance of predation comes from studies of pelagic zooplankton communities (Brooks and Dodson

1965, Wells 1970, Zaret 1980, Neil 1981, Carpenter et al. 1987); results of

studies regarding the role of predation in freshwater benthic communities,

however, are equivocal. Bohanan and Johnson (1983), Morin (1984), and

Gilinsky (1984) present evidence that predators affect benthic community

structure, whereas Thorp and Bergey (1981) argue that the occurrence of

keystone predators in freshwater benthic communities should be rare given 1)

the structural heterogeneity in freshwater benthic systems compared to the

rocky intertidal, 2) the complexity of freshwater benthic community food webs,

3) the opportunity for greater resource partitioning in the littoral zone of lakes

compared to the rocky intertidal, and 4) the absence of opportunities for

92 93 competitive subordinates to exploit open resources because of the localized nature of predation in time and space. I present results from field experiments done in a structurally complex environment, that show that a single predator species, pumpkinseed-aunfish (Lenomis gibbosus). can influence the structure of a freshwater benthic prey assemblage, i.e., snails.

In general, snails are intimately associated with structurally complex macrophyte habitats (Mason 1978, Lamarche et al. 1982, Aldridge 1983).

Some snails show species-specific macrophyte preferences (Pip and Stewart

1976, Pip 1978); the number of snail taxa in ponds in Sweden is positively correlated with the number of macrophyte species (Bronmark 1985). The close relationship between snails and macrophytes, suggests a high level of resource partitioning.

Experimental evidence exists showing the importance of habitat structural complexity in influencing predator-prey interactions in macrophyte habitats by providing refuges for prey (Savino and Stein 1982, Crowder and

Cooper 1982). But, in laboratory experiments (Chapter I), I showed that pumpkinseed preferences for snails remain unchanged over a range of habitat types, regardless of whether pumpkinseeds forage for snails over bare

sediments or in dense macrophytes. 94 Traditionally, physicochemical factors have long been recognized as the major determinants of snail abundance and distribution (Boycott 1936, Macan

1950, Russell-Hunter 1978, Okland 1983), but Lodge et al. (1987) propose a

conceptual model suggesting that physicochemical factors are most important in determining broad geographic distributions and that biotic factors, such as food preferences, competition, and predation, are more important in

determining abundance and distribution of snails among and within lakes.

Recent work on snails in Oklahoma streams suggests that abiotic and biotic factors, including predators, operate at different spatial scales to influence

abundance and distribution of snails (Crowl and Schnell 1990).

Pumpkinseeds are the major snail predator in vegetated portions of the

littoral zone in northern latitude lakes (Baker 1928, Osenberg and Mittelbach

1989), although many species of flsh include at least some snails in their diets

(Baker 1928). Pumpkinseeds are highly specialized predators of snails; a large

portion of their diet (Sadzikowski and Wallace 1976, Laughlin and Werner

1980, Mittelbach 1984, Osenberg and Mittelbach 1989) consists of snails.

Pumpkinseeds crush snails with their specialized before

consuming the soft body parts (Osenberg and Mittelbach 1989). Because snail

shell strength increases with size within a species, and varies among species

at a given size (Stein et al. 1984, Osenberg and Mittelbach 1989),

pumpkinseeds show strong preferences for snails based on shell characteristics 95 (Chapter I). Preferences are related to profitabilities associated with choosing among snail types, i.e., sizes and species (Osenberg and Mittelbach 1989,

Chapter II). Predictions from pumpkinseed foraging models (Osenberg and

Mittelbach 1989, Chapter II) and results from laboratory experiments examining snail choice by pumpkinseeds (Chapter I) suggest that because of pumpkinseed preference for large, weak-shelled species more of these snail species should be found in lakes with few pumpkinseeds.

Because pumpkinseeds are highly selective predators, I expected that adding pumpkinseeds to, or removing pumpkinseeds from, areas in lakes would result in measurable changes in snail species composition. I performed a 2-year caging experiment using enclosures, exclosures, and cageless controls.

Experiments were done in two lakes: one with few pumpkinseeds and one with historically high numbers of pumpkinseeds. Using this design, I planned the experiment such that in the low pumpkinseed density lake (LPDL), exclosures would mimic the natural predator densities of the lake or controls, whereas the enclosures would reflect predator densities found in the high pumpkinseed density lake (HPDL). Conversely, in the HPDL, I planned the experiment such that enclosures would mimic predator densities in the controls and that exclosures would reflect conditions of the low pumpkinseed density lake. 96 Methods

I worked in two mesotrophic lakes located in the Northern Highlands

State Forest, Vilas County, Wisconsin, USA. The low pumpkinseed density lake (LPDL), Round Lake (46°10’07"N 89°47,13MW), was 52 ha and had a maximum depth of 8 m; the high pumpkinseed density lake (HPDL), Mann

Lake (45°59’59"N 89°39’55"W), was 112 ha and had a maximum depth of 7 m.

Both lakes had dense beds of aquatic macrophytes, predominately Elodeas p p ..

Mvriophvllum spp., and Potamogeton spp. Densities of pumpkinseeds per

100 m of electrofishing effort (x ± 1 SE) in the low pumpkinseed density lake ranged from 2±0.6 in 1984 to 5±1.9 in 1987; in the high pumpkinseed density lake, densities ranged from 23±2.6 in 1984 to 11±5 in 1987. The decline in the high pumpkinseed density lake from 1984-1987 was most likely due to a winterkill during winter, 1985-1986.

Cages, 3 x 3 x 1.9 m, were constructed of wood framing and aluminum window screening (* 1.2 mm mesh size) and were held in place using a combination of wood stakes pounded into the substrate and cement blocks suspended from ropes attached at each cage comer. A 15 cm wide strip of aluminum flashing attached to the bottom edge sealed the cage to the lake bottom. Cageless controls were 3 x 3 m areas adjacent to cages. 97 Following a randomized block design, cages and controls were placed in groups, in the littoral zone at =1.2 m depth, at five locations in each lake.

Cages were put in place April 29-May 9, 1986. Fish were immediately removed from each cage using a combination of seines, traps, and electrofishing gear. The first snail samples were taken May 21-22, and each enclosure was stocked with three pumpkinseeds (mean ± 1 SD; 141 ± 4.7 mm

TL, 65 ± 7.6 g wet mass) on May 23. Cages and controls were sampled at

3-week intervals, May-October, during 1986; samples were taken at 6-week intervals, May-September, 1987. To allow experiments to occur over two summers, cages were built in two parts, such that they extended above the surface during summer, but remained below the ice during winter. Thus, at the end of October 1986, after the last sampling date but before ice-on, all fish were removed from enclosures, the upper section of each cage was removed, and the remaining 45 cm tall bottom section was covered with screen. In spring 1987, covers were removed, the upper sections of cages were replaced, and each enclosure was stocked with three pumpkinseeds (mean ± 1 SD; 151

± 5.1 mm TL, 77 ± 11.4 g wet mass).

During 1987,1 added yellow perch (Perea flavescens) enclosures to three

blocks in the LDPL because snail abundances and species composition

appeared to diverge between exclosures and controls during the first year in

this lake. The pattern suggested that either fish other than pumpkinseed, e.g., 98 yellow perch, were eating snails or that the densities of pumpkinseeds in the

LPDL were sufficient to impact snails. Addition of yellow perch enclosures allowed me to determine whether yellow perch, the most abundant fish in controls in the low pumpkinseed density lake, influenced snail assemblage structure or whether pumpkinseed densities in the LPDL were high enough to influence snail assemblage structure.

To estimate snail densities, I took four core samples per cage and control, and one sample from each cage wall on each sampling date. All samples were taken using scuba. The core sampler was designed to allow a diver to extract a 182 cm2 area of substrate and all macrophytes emanating from it. The long axis of the corer was hinged to allow the diver to encircle vegetation at the substrate. To take a sample using the corer, a diver encircled the macrophytes at their base, pressed the corer into the substrate, slid a metal plate into the bottom to secure the substrate, and unraveled a zippered mesh bag attached to the top of the corer to capture all snails on vegetation.

The wall sampler was constructed from a small dustpan covered with nylon

mesh glued to all but its leading edge. Snails in a 150-mm wide strip were

sampled by running the leading edge of the dustpan up the cage wall from the

bottom to the surface, capturing all snails that fell in the mesh enclosure.

Core samples were sieved in the field (0.1 mm mesh) to remove fine sediments

and then preserved with 10% buffered formalin; later, core samples were 99 rinsed and picked for snails. Wall samples and snails picked from core samples were preserved in 70% ETOH. All snails were identified to species and counted. When more than 50 individuals of a given species were in a sample, at least 50 individuals were measured; otherwise, all snails were measured. Snails less than =6 mm maximum shell dimension were measured to the nearest 0.05 mm using a compound microscope fitted with an ocular micrometer; snails greater than =6 mm were measured to the nearest 0.1 mm using calipers. Biomass was estimated using regressions of crushing resistance on snail length to estimate crushing resistance, and then, solving for snail mass using regressions of crushing resistance on snail mass found in

Osenberg and Mittelbach (1989); these regressions were used to estimate relative crushing resistance of snails as well.

Data were analyzed using univariate repeated-measures ANOVAs (SAS

Institute Inc. 1988). Analyses were based on the mean number of snails/m2 per

species for each cage and control. For cages, densities were calculated by

dividing the estimated number of snails on cage walls and on natural

substrates by the area enclosed by each cage; for controls, densities were

simply based on the mean of densities in core samples. All data were log10(x

+ 1) transformed to make the distribution of errors more normal and to reduce

variance heterogeneity. This transformation provided the additional benefit 100 of making treatments with similar net rates of growth, but different initial

densities, additive.

I used repeated measures ANOVA rather than MANOVA because the

number of repeated measures, i.e., sampling dates, exceeded the rank of the

design matrix; to perform a MANOVA, I would have had to exclude data from

four sampling dates. Also, ANOVAs are more powerful than MANOVAs when

sample size is small (Milliken and Johnson 1984). Because the number of

repeated measures exceeded the rank of the design matrix, I could not perform

a sphericity test (Mauchly 1940) to determine if variance-covariance matrices

satisfied the Huynh-Feldt (1970) criteria of Type-H structure. Thus, I

conservatively used one of the e-adjusted univariate tests that Heame et al.

(1983) and Milliken and Johnson (1984) suggest for situations when the

sphericity test is rejected. I report Huynh-Feldt E-adjusted error degrees of

freedom (1976) because they are generally more powerful than F-tests using

Greenhouse and Geisser’s (1959) correction, and for the values of e calculated

from my data, they provide adequate protection against Type-I errors (Huynh

and Feldt 1976).

Significant effects were based on orthogonal contrasts comparing high

pumpkinseed density treatments to low pumpkinseed density treatments

within a lake. For example, for the HPDL, I tested whether the 101 no-pumpkinseed treatment differed from pumpkinseed treatments by comparing exclosures to enclosures and controls. Within the pumpkinseed treatments, I contrasted enclosures to controls. Similarly, in the LPDL, I tested whether low pumpkinseed treatments differed from high pumpkinseed treatments by comparing exclosures and controls to enclosures. Within the low pumpkinseed treatments, I contrasted the no-pumpkinseed treatment, i.e.,

exclosures, to the low pumpkinseed treatment, i.e., controls. Additionally, for

data from the LPDL 1987,1 compared no fish to fish treatments by contrasting

exclosures against pumpkinseed enclosures, yellow perch enclosures, and

controls. Within the fish treatments, I compared no pumpkinseed treatments

to pumpkinseed treatments by contrasting yellow perch enclosures to pumpkinseed enclosures and controls. Lastly, I compared the

high-pumpkinseed treatment, i.e., pumpkinseed enclosures, to the low

pumpkinseed treatment, i.e., controls.

I determined when pumpkinseeds had their greatest effect on each

species by using ANOVAs to compare rates of change between successive

sampling dates, i.e., log10(x + 1) densities on sampling

date i + 1 minus log10(x + 1) densities on sampling date i. To determine the

cumulative effect of treatments on snails, I used ANOVAs to contrast the

overall net rate of increase among the different predator treatments, i.e., 102 log10(x + 1) density on the last sampling date minus log10(x + 1) density on the first sampling date. 103 Results

The snails

Of the 12 species of snails collected during 1986-1987; 11 occurred in both lakes. But, of the 112,000 snails in samples, only 10 were Lvmnaea haldemani and 36 were Helisoma trivolvis: thus, I excluded them from analyses (Table 7).

Maximum snail size and estimated crushing resistance varied markedly among species. The smallest snails collected were < 0.5 mm, and maximum adult size, for all species, ranged from 4-18 mm (Table 7); the largest snails were Helisoma and Phvsa. Mean size varied seasonally within species (Figs.

18 and 19); individuals of most species appeared to live for 1 year, though there was some overlap among cohorts. Within a species, mean size from dates with the largest standard deviations represent dates when both adults and juveniles were present in samples; however, for Phvsa and Helisoma. large standard deviations occurred because of the range of sizes within a cohort as well as variation in size due to the presence of two cohorts. Phenologies were such that among species, maximum adult size was reached at different times of the year. 104

Table 7. Maximum size, i.e., length and mass, of snail taxa, collected in Mann and Round Lakes, Wisconsin. Maximum length was measured along the axis of the shell’s largest dimension.

Maximum length (mm) Maximum biomass (mg) Taxonomic group Mann Lake Round Lake Mann Lake Round Lake Prosobranchs Amnicola walkeri 3.6 3.0 0.8 0.5 Marstonia lustrica 4.2 4.1 0.9 0.8 Amnicola limosa 4.7 4.8 1.5 1.6 Valvatidae Valvata tricarinata 4.1 3.7 1.4 1.0 Pulmonates Planorbidae Gvraulus parvus 5.1 4.0 0.7 0.4 Gvraulus deflectus 6.9 5.8 2.3 1.5 Promenetus exacuous 6.4 6.4 2.3 2.3 Helisoma anceps 11.7 12.9 14.8 19.3 Helisoma campanulata 16.5 13.6 24.8 15.5 Physidae Phvsa sp. 17.6 18.3 29.5 32.6 6 - 16 Helisoma ^ Valvota tricarinata 4- campanulata ^ -----> 14 T _ I I^ \ T 2- 12

0- 10

6- / \ Gyraulus deflectus 8 • \ .• 4- 6 2- vV y v y i 4 0- 2 Gyraulus parvus 6- 0 T 4- Helisoma anceps 10 • T r/N 1 1 2 - VrTtT'’ 8 0' 6 Marstonia lustrica 6 4

4' 2 2 * * * \ ■ 0 V*-* V ’ 0 16 Amnicola walkeri 6 14

4 12

2 10

0 8 Amnicola limosa 6 6

4 4

2 2 0 0 MJJASO MJJAS MJJASO MJJAS 1986 1987 1986 1987

18. Mean size of snails (± 1 SD) collected in the high pumj lake, Mann Lake, Wisconsin, 1986-1987. Figure 19. Mean size of snails (± 1 SD) collected in the low pumpkinseed pumpkinseed low the in collected SD) 1 (± snails of size Mean 19. Figure est ae on ae icni, 1986*1987. Wisconsin, Lake, Round lake, density

Mean Size (mm) ♦HI . • ''* " T • i . t i H Valvata tricarinata Amnicolawalker! Gyraulusdeflectus mioa limosa Amnicola Gyraulusparvus Marstonialustrica Promenetus exacuaous Promenetus I1 • " ? 1 I 1 96 97 96 19B7 1986 1987 1986 S A 'T m, J A 0 S A J J M Helisomaanceps Helisomacampanulata Physa MjJAS -• -• 14 -•16 --4 --6 -•8 --8 --12 --10 --10 ■•14 --2 --6 ■■4 ■•4 ■8 •10 -2 •2 •6 12 106 107 Estimated adult biomass ranged 0.4-33 mg dry tissue mass (Table 7); snails with the largest mass were Helisoma and Phvsa. Within the size range susceptible to pumpkinseed predation, i.e., those less £ 13 mm (S. Klosiewski, personal observation), maximum biomass among species ranged from 0.4-

20 mg; within this restricted range, biomass of the largest Helisoma and Phvsa were at least eight times greater than biomass of the largest adults from other

taxa.

Generally, species that reached the largest size as adults, i.e., Helisoma

spp. and Phvsas p . (Table 7), had the weakest shells at a given size, regardless

of whether size was based on biomass or shell length (Table 8); only

Promenetus exacuous had weaker shells. For example, estimated crushing

resistance for 10-mm Phvsa was similar to that of 4-mm Valvata tricarinata

or Gvraulus parvus, and 4-mm Amnicola limosa had shells almost 25%

stronger than those of 10-mm Phvsa. Gvraulus parvus at 0.5 mg had stronger

shells than Phvsa (5 mg) with 10 times greater mass.

At the start of the experiment, snails with the weakest shells, i.e.,

Promenetus exacuous. Helisoma campanulata. Helisoma anceps. and Phvsa

sp., were conspicuously absent in samples from the high pumpkinseed density

lake; however, they accounted for more than 8% of the total biomass in the low

pumpkinseed density lake (Fig. 20). In both lakes, the three hydrobiids, 108

Table 8. Crushing resistance for snails of a given size estimated from regressions of crushing resistance on dry body mass and length of maximum shell dimension (from Osenberg and Mittelbach 1989). Species are ranked based on shell strength, from weakest to strongest, for snails with dry body mass equal to 0.5 mg; similarly, ranks are provided for snails with dry body mass equal to 10 mg and for snails with shell size equal to 4 and 10 mm.

Crushing resistance (newtons)

Snail mass Snail length

Species 0.5 mg 5 mg (rank) 4 mm (rank) 10 mm (rank)

Promenetus exacuous 0.8 -- 0.8 (1) - -

Helisoma camDanulata 0.9 12.1 (2) 1.5 (2) 18.7 (2)

Helisoma ancens 1.9 13.4 (3) 2.8 (3) 23.5 (3)

Phvsa sd . 4.0 11.0 (1) 4.3 (4) 12.5 (1)

Gvraulus deflectus 4.9 - - 5.2 (5) --

Marstonia lustrica 5.4 -- 6.4 (6) --

Valvata tricarinata 5.9 -- 12.7 (8) --

Amnicola walkeri 6.4 - - 10.3 (7) --

Amnicola limosa 9.2 -- 15.9 (10) - -

Gvraulus Darvus 16.8 -- 12.8 (9) -- 109 Amnicola limosa. Amnicola walkeri. and Marstonia lustrica. accounted for

> 80% of total snail biomass (Fig. 20); generally, these species have some of the

strongest shells (Table 8). Both lakes were dominated by small strong-shelled

species; large, weak-shelled species were present in the LPDL, but were absent

from the HDPL.

Response of snail assemblages to predator density manipulations

Six of nine species in the high pumpkinseed density lake and five of ten

species in the low pumpkinseed density lake responded to predator

manipulation as determined by significant (p < 0.05) time x treatment

interactions in repeated measures ANOVAs. Weak-shelled species and those

that reached a large size as adults showed the most significant responses to

predator manipulations.

High pumpkinseed density lake

Comparison of pumpkinseed vs no pumpkinseed treatments - In the high

pumpkinseed density lake, abundances for those species with the weakest

shells, i.e., Marstonia lustrica. Gvraulus deflectus. Helisoma anceps. Helisoma

campanulata. and Phvsa sp. changed at different rates over time in the

no-pumpkinseed treatment compared to pumpkinseed treatments, i.e., 110

Round Lake II I I H ' l f T T T m IIII 1.11 Mann Lake

£ 0 .4 -

■=» *1 PEX HCA HAN PHY GDE MLU VTR AWA ALI GPA Snatl Species

Figure 20. Snail species composition, based on proportion of total biomass estimated from samples at the start of the experiment (May 1986), in the low pumpkinseed density lake, Round Lake, and the high pumpkinseed density lake, Mann Lake, Wisconsin. PEX = Promenetus exacuous. HCA = Helisoma camnanulata. HAN » Helisoma anceps. PHY = Phvsa sp., GDE = Gvraulus deflectus. MLU = Marstonia lustrica. VTR = Valvata tricarinata. AWA = Amnicola walkeri. ALI = Amnicola limoaa. and GPA = Gvraulus parvus. I l l exclosures compared to enclosures and controls (Table 9, Fig. 21). Although these species showed an overall predator manipulation effect, I detected few differences between no-pumpkinseed and pumpkinseed treatments when I compared rates of change for each successive date comparison (ANOVA, F1>8, p < 0.05; see Fig. 21); the most differences I found for any species was 3 of 11 comparisons. For intervals when comparisons differed, snail abundance increased at a faster rate, or decreased at a slower rate, in the exclosures compared to enclosures and controls for all but two comparisons. Of the exceptions, Marstonia lustrica decreased in exclosures compared to predator treatments between the fourth and fifth sampling dates; Phvsa decreased in exclosures relative to predator treatments between the last sampling date 1986 and the first of 1987. That few differences were found was undoubtedly due, at least in part, to low statistical power. Given that rates of increase from day to day are additive, a comparison of overall net rate of increase among treatments should be more robust.

Overall net rates of increase for Helisoma ancens (ANOVA. F18 = 8.13, p = 0.02), Helisoma campanulata (ANOVA, F18 = 6.2, p = 0.04), and Phvsa

(ANOVA, F18 = 32.7, p = 0.0004) were higher in exclosures than in treatments

with pumpkinseeds. As adults, Helisoma spp. and Phvsa were larger than any

other taxa found in the HDPL, and they generally had the weakest shells; only 112

Table 9. Repeated-measures ANOVAs done on log10(x + 1) densities of snails in exclosures (Exc), enclosures (Enc), and cageless controls (C) in Mann Lake, Wisconsin, i.e., the high pumpkinseed density lake. Comparisons between high- and no-predator treatments, i.e., C and Enc vs Exc, and within high-predator treatments, i.e., C vs Enc, were done using orthogonal contrasts. P values were calculated using e-adjusted error degrees of freedom (Huynh and Feldt 1976). Amnicola limosa Amnicola walkeri

Source df MS FP df MS FP

Treatment 2 10.79 22.07 0.0006 2 5.76 9.12 0.0087 Block 4 8.92 18.24 0.0004 4 6.66 10.53 0.0028 Error A 8 0.49 8 0.63 Time 11 1.16 8.18 0.0001 11 4.83 23.93 0.0001 Time x Treatment 22 0.35 22 0.30 C and Enc vs Exc 11 0.18 1.26 0.2614 11 0.23 1.12 0.3525 C vs Enc 11 0.52 3.66 0.0003 11 0.37 1.84 0.0590 Time x Block 44 0.21 1.49 0.0562 44 0.23 1.16 0.2753 Error B 88 0.14 88 0.20

Marstonia lustrica Valvata tricarinata

Source df MS FP df MS FP

Treatment 2 11.66 9.20 0.0084 2 3.54 3.34 0.0883 Block 4 16.70 13.18 0.0013 4 24.44 23.07 0.0002 Error A 8 1.27 8 1.06 Time 11 2.86 12.38 0.0001 11 0.56 2.05 0.0534 Time x Treatment 22 0.61 22 0.31 C and Enc vs Exc 11 0.63 2.71 0.0047 11 0.38 1.39 0.2156 C vs Enc 11 0.59 2.55 0.0075 11 0.24 0.88 0.5411 Time x Block 44 0.32 1.36 0.1092 44 0.32 1.17 0.2879 Error B 88 0.23 88 0.27 113

Table 9. (continued) Gvraulus parvus Gvraulus deflectus

Source df MS F P df MS F P

Treatment 2 0.43 1.32 0.3204 2 0.11 1.14 0.3665 Block 4 2.49 7.72 0.0075 4 0.09 0.90 0.5056 Error A 8 0.32 8 0.10 Time 11 4.05 18.53 0.0001 11 0.21 3.07 0.0038 Time x Treatment 22 0.27 22 0.13 C and Enc vs Exc 11 0.22 1.01 0.4412 11 0.17 2.51 0.0155 C vs Enc 11 0.32 1.46 0.1598 11 0.08 1.12 0.3597 Time x Block 44 0.36 1.65 0.0232 44 0.11 1.63 0.0417 Error B 88 0.22 88 0.07

Helisoma anceos Helisoma camnanulata

Source df MS F P df MS F P

Treatment 2 6.75 5.18 0.0360 2 1.42 5.29 0.0344 Block 4 2.58 1.98 0.1899 4 1.61 5.98 0.0158 Error A 8 1.30 8 0.27 Time 11 0.90 7.80 0.0001 11 0.34 3.25 0.0052 Time x Treatment 22 0.32 22 0.15 C and Enc vs Exc 11 0.58 5.03 0.0001 11 0.26 2.46 0.0270 C vs Enc 11 0.07 0.58 0.8361 11 0.04 0.41 0.8982 Time x Block 44 0.27 2.33 0.0005 44 0.22 2.09 0.0088 Error B 88 0.12 88 0.10

Phvsa s d .

Source df MS F P

Treatment 2 7.85 14.51 0.0022 Block 4 0.86 1.58 0.2688 Error A 8 0.54 Time 11 1.41 12.92 0.0001 Time x Treatment 22 0.52 C and Enc vs Exc 11 0.80 7.32 0.0001 C vs Enc 11 0.25 2.26 0.0202 Time x Block 44 0.14 1.29 0.1619 Error B 88 0.11 114

1000 1000 Gyraulus deflectus Valvata tricarinata 100 □— oControl 100 •— • Exclosure *— ^Enclosure - Pumpkinseed

Gyraulus parvus 1000 100

100

10 --

Marstonia lustrica Helisoma campanulata 100

100 -

Helisoma anceps IQ- 100

Amnicola walkeri **/ 1000 P h y sa 100 100

Amnicola limosa

M J J A S 0 M J J A S M 1986 1 9 8 7 1 9 8 6 1987

Figure 21. Mean abundance of snails in enclosures, exclosures, and controls in Mann Lake, HPDL, 1986-1987. ** indicates intervals when exclosures differed from enclosures and controls, * indicates intervals when enclosures differed from controls. 115 Promenetus exacuous had weaker shells, but too few were collected in samples to judge their response to predator manipulation.

Within-numpkinseed treatment comparisons: enclosures vs controls - Within pumpkinseed treatments, abundance of Amnicola limosa. Marstonia lustrica and Phvsa changed at different rates (Repeated-measures ANOVAs, p < 0.05) in enclosures compared to controls (Table 9); results for Amnim1a walkari wprs marginally insignificant (p = 0.059). Few successive date comparisons within a species were significant (ANOVA, F18, p < 0.05; see Fig. 21). From May 1986 to September 1987, net rates of increase for Amnicola limosa (ANOVA,

F18 = 16.8, p = 0.003) and Phvsa (ANOVA, F1>8 = 8.58, p = 0.02) were higher in controls than enclosures.

Low pumpkinseed density lake

Low pumpkinseed vs high pumpkinseed treatment comparisons - In the low pumpkinseed density lake, only Amnicola limosa. Valvata tricarinata. and

Helisoma anceps responded to manipulations as I expected, i.e., low

pumpkinseed treatments (exclosures and controls) differed from the high

pumpkinseed treatment (enclosures) (Repeated-measures ANOVAs, see

Table 10; Fig. 22). Of those species showing differences, rates of increase were

higher in low-pumpkinseed than high-pumpkinseed treatments for three 116 Table 10. Repeated-measures ANOVAs done on log10(x + 1) densities of snails in exclosures (Exc), enclosures (Enc), and cageless controls (C) in Round Lake, Wisconsin, i.e., the low pumpkinseed density lake. Comparisons between low- and high-predator treatments, i.e., C and Exc vs Enc, and within low-predator treatments, i.e., C vs Exc, were done using orthogonal contrasts. P values were calculated using e-adjusted error degrees of freedom (Huynh and Feldt 1976). Amnicola limosa Amnicola walkeri

Source df MS F P df MS F P

Treatment 2 12.02 16.00 0.0016 2 0.37 1.61 0.2594 Block 4 2.73 3.64 0.0567 4 4.59 20.06 0.0003 Error A 8 0.75 8 0.23 Time 11 1.76 16.69 0.0001 11 1.20 20.56 0.0001 Time x Treatment 22 0.82 22 0.06 C and Exc vs Enc 11 1.57 14.89 0.0001 11 0.05 0.94 0.5067 C vs Exc 11 0.07 0.63 0.7954 11 0.06 1.09 0.3810 Time x Block 44 0.12 1.10 0.3530 44 0.12 2.01 0.0027 Error B 88 0.11 88 0.06 Marstonia lustrica Valvata trinarinata

Source df MS FP df MS F P

Treatment 2 2.54 4.19 0.0570 2 6.41 4.29 0.0541 Block 4 20.64 33.96 0.0001 4 28.19 18.89 0.0004 Error A 8 0.61 8 1.49 Time 11 2.64 5.98 0.0001 11 3.50 16.22 0.0001 Time x Treatment 22 0.35 22 0.38 C and Exc vs Enc 11 0.40 0.91 0.5390 11 0.47 2.19 0.0297 C vs Exc 11 0.29 0.66 0.7705 11 0.29 1.34 0.2265 Time x Block 44 0.70 1.59 0.0330 44 0.22 1.00 0.4854 Error B 88 0.44 88 0.22 117 Table 10. (continued) Gvraulus parvus Gvraulus deflectus

Source df MS F P df MS FP

Treatment 2 0.19 0.11 0.8982 2 2.69 3.46 0.0829 Block 4 4.20 2.44 0.1321 4 7.38 9.49 0.0039 Error A 8 1.72 8 0.78 Time 11 1.25 5.95 0.0001 11 3.77 13.92 0.0001 Time x Treatment 22 0.46 22 0.32 C and Exc vs Enc 11 0.15 0.72 0.7130 11 0.28 1.05 0.4114 C vs Exc 11 0.76 3.61 0.0003 11 0.35 1.28 0.2499 Time x Block 44 0.41 1.95 0.0040 44 0.64 2.38 0.0003 Error B 88 0.21 88 0.27

Helisoma anceDs Helisoma camnanulata

Source df MS FP df MS FP

Treatment 2 3.64 7.46 0.0149 2 13.56 22.11 0.0006 Block 4 5.05 10.35 0.0030 4 0.90 1.47 0.2967 Error A 8 0.49 8 0.61 Time 11 0.91 4.04 0.0001 11 3.98 13.11 0.0001 Time x Treatment 22 0.51 22 0.43 C and Exc vs Enc 11 0.68 3.03 0.0018 11 0.34 1.11 0.3644 C vs Exc 11 0.34 1.52 0.1370 11 0.52 1.72 0.0824 Time x Block 44 0.51 2.25 0.0006 44 0.36 1.18 0.2506 Error B 88 0.23 88 0.30

Phvsa sp. Promenetus exacuous

Source df MS FP df MS F P

Treatment 2 20.21 98.14 0.0001 2 1.26 1.10 0.3782 Block 4 2.30 11.18 0.0023 4 13.99 12.19 0.0017 Error A 8 0.21 8 1.15 Time 11 2.56 12.31 0.0001 11 3.29 7.61 0.0001 Time x Treatment 22 0.38 22 0.39 C and Exc vs Enc 11 0.33 1.59 0.1143 11 0.37 0.85 0.5918 C vs Exc 11 0.43 2.04 0.0334 11 0.41 0.95 0.4938 Time x Block 44 0.32 1.53 0.0461 44 0.48 1.11 0.3372 Error B 88 0.21 88 0.43 118 successive date comparisons for Amnicola limosa and Valvata tricarinata. and for one comparison for Helisoma ancens (ANOVA, F1>8, p < 0.05; see Fig. 22).

During May 1986 through September 1987, net rates of increase were higher in low-pumpkinseed treatments than high-pumpkinseed treatments for

Amnicola limosa (ANOVA, F18 = 72.31, p = 0.0001) and Helisoma anceps

(ANOVA, Fii8 = 0.03); results for Valvata tricarinata were marginally insignificant (ANOVA, F1>8 = 3.8, p = 0.09). Amnicola limosa declined substantially in enclosures compared to exclosures and controls. Helisoma anceps increased in exclosures, remained at about the same density in controls, and declined in exclosures.

Within lnw.nnmnkinseed treatment comparisons: no pumpkinseeds vs low pumpkinseeds - Within the low-pumpkinseed treatments in the low pumpkinseed lake, the rate of change for Gvraulus parvus and Phvsa differed between exclosures and controls; the results for Helisoma camnanulata were marginally insignificant (Repeated-measures ANOVA, Table 10). Gvraulus parvus declined faster in controls than exclosures between the first and second

sampling dates (ANOVA, F18 = 14.23, p = 0.005). Phvsa declined at a faster rate in exclosures compared to controls between October 1986 and May 1987

(ANOVA, F18 » 6.59, p = 0.03; see Fig. 22); this was likely due to the low

numbers of Phvsa in controls at the end of October 1986 rather than the result

of a predator effect. But overall, net rates of increase for Gvraulus parvus lOOl 1000 d e f l e ^ u ^ Promenetus exacuous 10' 100

1; 10

1 100 ...... Valvata tricarinata 10 •1000 ** ^ ___ _ '' Gyraulus parvus 1 •100

Marstonia lustrica 100 r 10 N—* 10 ; 1

Helisoma campanulata 1 ■100

1000 ■10

:1 100 a-----aC o n tro l * •— • Exclosure a —‘Enclosure — Pumpkinseed Helisoma anceps 10 a-----‘ Enclosure — Yellow Perch r 1 0 0 ** Amnicola walkeri 1; •10

n-mn ** 1000 •1 P hy sa 100 ■100

10 \ 10 Amnicola limosa 1 ; 1 — i— i— i— i— i— i— i— i— i— i— i— l<—* * t l i t 1 1 M J J A S 0 MJJAS MJJASO MJJAS 1986 1987 1986 1987

!. Mean abundance of snails in enclosures, exclosures, and c< ontr< Lake, LPDL, 1986-1987. ** indicates intervals when exclosuri es a iffered from enclosures, * indicates intervals when exclosures d ;rols. 120 (ANOVA, F1i8 = 2.52, p = 0.15) or Phvsa (ANOVA. Flf8 = 0.19, p = 0.68) did not differ between exclosures and controls.

Fish vs no fish treatment comparisons - During 1987, the densities of Amnicola limosa. in treatments without fish changed at different rates than those with fish, i.e., as determined by repeated-measures ANOVA; over time, exclosures differed from yellow perch enclosures, pumpkinseed enclosures, and controls

(Table 11). Overall, the net rate of increase was greater in exclosures than in treatments with fish (ANOVA, F16 = 9.79, p = 0.02). Results for all other taxa were insignificant.

Pumpkinseed vs no pumpkinseed treatment comparisons - Seven of ten species, exhibited significant time by treatment interactions, when using contrasts to compare treatments with pumpkinseeds to treatments that had fish but no pumpkinseeds, i.e., pumpkinseed enclosures and controls vs yellow perch enclosures (Table 11). Rates of change in abundance for Amnicola limosa.

Amnicola walkeri. Valvata tricarinata. Gvraulus deflectus. Helisoma camnanulata. Phvsa. and Promenetus exacuous differed between treatments with and without pumpkinseeds over time. And overall, except for Promenetus

exacuous. the net rate of increase was lower in pumpkinseed treatments than in treatments with fish but without pumpkinseeds (ANOVA, F1>6, p < 0.05). 121 Table 11. Repeated-measures ANOVAs done on log10(x + 1) densities of snails in exclosures, enclosures, and cageless controls in Round Lake, Wisconsin, i.e., the low pumpkinseed density lake. Fish vs No Fish compares controls and yellow perch and pumpkinseed enclosures to exclosures; PS vs No PS compares controls and pumpkinseed enclosures to yellow perch enclosures; High PS vs Low PS compares pumpkinseed enclosures to controls. P values were calculated using £-adjusted error degrees of freedom (Huynh and Feldt 1976).

Amnicola limosa Amnicola walkeri

Source df MS FP df MS F P

Treatment 3 4.87 20.90 0.0014 3 0.57 3.83 0.0760 Block 2 0.02 0.07 0.9356 2 2.72 18.46 0.0027 Error A 6 0.23 6 0.15 Time 3 1.91 20.52 0.0001 3 0.56 9.29 0.0006 Time x Treatment 9 1.17 9 0.10 Fish vs No Fish 3 0.84 9.06 0.0007 3 0.08 1.33 0.2966 PS vs No PS 3 0.83 8.93 0.0008 3 0.19 3.23 0.0470 High PS vs Low PS 3 1.85 19.81 0.0001 3 0.03 0.49 0.6965 Time x Block 6 0.09 0.98 0.4663 6 0.02 0.40 0.8689 Error B 18 0.09 18 0.06

Marstonia lustrica Valvata tricarinata

Source df MS FP df MS FP

Treatment 3 1.34 1.54 0.2980 3 3.93 5.48 0.0374 Block 2 5.89 6.74 0.0293 2 1.26 1.76 0.2508 Error A 6 0.88 6 0.72 Time 3 4.16 8.25 0.0012 3 2.18 10.14 0.0004 Time x Treatment 9 0.34 9 0.51 Fish vs No Fish 3 0.44 0.87 0.4729 3 0.12 0.54 0.6633 PS vs No PS 3 0.35 0.69 0.5676 3 0.83 3.87 0.0268 High PS vs Low PS 3 0.24 0.48 0.7035 3 0.58 2.70 0.0761 Time x Block 6 0.32 0.64 0.6946 6 0.09 0.41 0.8596 Error B 18 0.50 18 0.22 122 Table 11. (continued)

Gvraulus parvus Gvraulus deflectus

Source df MS F P df MS FP

Treatment 3 1.27 0.96 0.4682 3 0.63 3.87 0.0746 Block 2 2.69 2.04 0.2104 2 0.46 2.85 0.1346 Error A 6 1.32 6 0.16 Time 3 0.38 1.28 0.3104 3 0.75 3.42 0.0398 Time x Treatment 9 0.52 9 0.36 Fish vs No Fish 3 0.90 3.00 0.0576 3 0.16 0.74 0.5397 PS vs No PS 3 0.64 2.16 0.1286 3 0.93 4.20 0.0203 High PS vs Low PS 3 0.02 0.07 0.9743 3 0.00 1.00 1.0000 Time x Block 6 0.07 0.23 0.9622 6 0.64 2.38 0.0003 Error B 18 0.30 18 0.22

Helisoma anceps Helisoma camnanulata

Source df MS F P df MS F P

Treatment 3 1.47 1.16 0.3988 3 6.31 16.59 0.0026 Block 2 3.67 2.88 0.1326 2 0.50 1.31 0.3379 Error A 6 1.27 6 0.38 Time 3 0.87 5.12 0.0098 3 2.83 24.03 0.0001 Time x Treatment 9 0.32 9 0.47 Fish vs No Fish 3 0.11 0.67 0.5838 3 0.32 2.79 0.0704 PS vs No PS 3 0.47 2.76 0.0723 3 0.77 6.53 0.0035 High PS vs Low PS 3 0.38 2.21 0.1216 3 0.33 2.77 0.0715 Time x Block 6 0.37 2.21 0.0898 6 0.16 1.38 0.2773 Error B 18 0.17 18 0.12 123 Table 11. (continued)

Phvsa sn. Promenetus exacuous

Source df MS FP df MS F P

Treatment 3 3.86 7.42 0.0192 3 1.40 1.36 0.3412 Block 2 1.01 1.95 0.2232 2 3.08 3.00 0.1251 Error A 6 0.52 6 1.03 Time 3 3.26 21.96 0.0001 3 2.81 9.40 0.0006 Time x Treatment 9 0.99 9 0.68 Fish vs No Fish 3 0.42 2.82 0.0682 3 0.59 1.98 0.1528 PS vs No PS 3 2.21 14.88 0.0001 3 1.40 4.68 0.0138 High PS vs Low PS 3 0.34 2.29 0.1130 3 0.06 0.20 0.8977 Time x Block 6 0.25 1.69 0.1806 6 0.45 1.49 0.2367 Error B 18 0.15 18 0.30 124 Low pumpkinseed vs high pumpkinseed treatment comparisons - Within the pumpkinseed treatments, i.e., enclosures and controls, only Amnicola limosa

showed significant time x treatment interactions in contrasts of low pumpkinseed treatments to high pumpkinseed treatments (Table 11). As in the HPDL, Amnicola limosa declined at a significantly faster rate in enclosures in comparison to controls (ANOVA, F16 = 23.3, p = 0.003). 125 Discussion

My objective in conducting this study was to determine if predators, i.e., pumpkinseeds and yellow perch, influence snail assemblage structure; in practice I chose to test whether the rate of change in species abundance differed among treatments. I interpreted these differences as evidence showing that pumpkinseeds can regulate the composition of snail assemblages. Using rate of change in abundance, or net rate of increase, as my metric to judge the role of pumpkinseeds makes intuitive sense when considered in the context of

Lotka-Volterra predator prey equations and models of predator-mediated coexistence, e.g., Cramer and May 1972, Roughgarden and Feldman 1975, Holt

1977, Yodzis 1977, and Caswell 1978. Regardless of model derivation, predators influence prey populations, and ultimately community composition, by changing the rate at which species increase in abundance, either due to the

direct effects of predation or indirect effects of predation resulting from

changes in the amount of inter- and intra-specific competition. Thus, my

metric should determine whether pumpkinseeds influence snail assemblage

structure.

Using proportional representation of species to describe a community,

based on abundance or biomass, seems equally intuitive given that it best

describes the community at a given time. However, its value diminishes when 126 species composition at any time is depends strongly on species specific phenologies associated with growth, death, and recruitment, and when these proportions must be compared to initial conditions such as was the case for my study. At most I would have been able to make one informative comparison using proportions, i.e., one between the first sample date in 1986 and the first sample date in 1987. The lack of independence between species being compared, makes using proportions disadvantageous when trying to determine which species are actually responding to predators. And, lack of independence reduces the chance of documenting significant effects especially if rare species are of interest (as they usually are), given that variances for rare species are strongly influenced by the variation in abundance those most numerous.

Though my experiments ran for 2 years, they were too short to show what kinds of assemblages result from different levels of predation, or from predator exclusion. Rather, they demonstrate that predators can influence structure by modifying net rates of increase of snail populations. For example,

Helisoma anceps. Helisoma campanulata. and Phvsa increased in abundance when predators were excluded, both in absolute terms and in relative terms, in both the HPDL and LPDL. But, there are no data available on snails species composition in fishless lakes suggest that large, weak-shelled species predominate in the absence of predation, though Lodge et al. (1987) speculate

on just such a pattern. The presence of the snail species with the weakest 127 shells in the LPDL, and their absence in the HPDL, at the start of the experiment, however, lends support to such speculation.

Results from laboratory experiments support the notion that large, weak-shelled snails should predominate in the absence of pumpkinseeds.

Generally, for molluscivorous fishes, time spent handling snails increases with shell strength (Stein et al. 1984, Chapter I). And of course, snail mass increases with shell size. Thus, snail predators should prefer large, weak-shelled snails over small, strong-shelled ones. There is an additional cost associated with shell strength, because the probability that a pumpkinseed

successfully crushes a snail decreases with increasing shell strength (Osenberg

and Mittelbach 1989, Chapter I). When given a choice of snails, pumpkinseeds

generally show a preference for large snails with weak shells, although shell

strength seems less important for large pumpkinseeds, i.e., those > 150 mm TL

(Chapter I).

In this study, size differences within a species, as well as size differences

among species, appeared to influence patterns of predation. Clearly, species

that reached a large size as adults, i.e., Phvsa and Helisoma spp., increased to

predator exclusion, but these species also had the weakest shells. For species

other than Phvsa and Helisoma spp., predators tended to have their greatest

effect at times when snails reached their maximum size (see Fig. 18, 19, 128 21,22). Snails in these species may be able to coexist with predators because

they are too small to be preyed on for a good portion of the year.

I suggest that pumpkinseeds have the potential to be a "keystone"

predator (Paine 1966) in northern Wisconsin lakes for a number of reasons.

Although many species of fish include snails in their diet, pumpkinseeds are

the predominant snail consumer in macrophyte habitats in Wisconsin (Baker

1928; S. Klosiewski, personal observation). Snails typically make up more

than 60% of the diet of adult pumpkinseeds (Sadzikowski and Wallace 1976,

Laughlin and Werner 1980, Mittelbach 1984, Osenberg and Mittelbach 1989).

Pumpkinseeds show strong preferences for prey; in laboratory experiments

(Chapter 1), I showed that pumpkinseeds exhibit relatively constant

preferences for prey over a wide range of relative abundances, even when the

ratio of abundances of snail types was skewed heavily in favor of the

non-preferred snail (Chapter I). Unknown though, is whether one or a few

species of snails predominate in the absence of predation because of their

superior competitive abilities. Brown (1982) for example, found little evidence

for resource overlap among snail species that occupy different habitats;

however, he did find evidence of competition between two species that occupied

a similar niche. Eisenberg (1966) and Brown (1982) demonstrate that

competition among snails leads to reduced fecundities. The potential for 129 competition and the highly selective nature of pumpkinseeds supports the idea of pumpkinseeds as "keystone" predators.

However, a "keystone" effect may be difficult to demonstrate for single-generation organisms such as snails, through experimentation, simply because 1-3 years is insufficient for competitive exclusion to occur (Lodge et al.

1987). I suspect that experimentation with other benthic invertebrates, with life-histories similar to snails, would be similarly unproductive.

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Appendix A. Summary of experimental conditions. Snail species were Amnicola limosa (AL), Lvmnaea emareinata (LE), Helisoma anceps (HA), Campeloma descisum (CD), and Phvsa sp. (PH).

Experiment Snail Fish # of # of Mean # Mean # Type Habitat Species Size(mm) Number Size(mm) Trials Fish Eaten Attacked

Multi-size, single species, equal densities

I. Sand AL 2 50 90 12 5 6.5 19.9 AL 4 50 120 34 7 12.5 26.6 150 18 6 23.9 33.4

II. Sand LE 3 25 90 15 7 9.5 17.2 LE 6 25 120 15 8 13.7 27.2 LE 9 25 150 14 7 16.5 31.1 LE 12 25

III. Macrophyte AL 2 50 90 2 2 2.5 10.5 AL 4 50 120 4 4 10.3 22.8 150 2 2 23.0 28.5

IV. Artificial LE 3 25 90 15 7 7.1 14.7 macrophyte LE 6 25 120 16 7 10.6 17.7 LE 9 25 150 20 7 12.9 17.2 LE 12 25

V. Macrophyte LE 3 25 90 11 6 4.6 9.3 LE 6 25 120 14 7 6.4 14.1 LE 9 25 150 14 7 11.0 17.1 LE 12 25

VI. Cobble LE 3 25 90 7 3 4.1 7.9 LE 6 25 120 7 4 7.3 11.1 LE 9 25 150 14 6 6.8 11.4 LE 12 25

144 145 Appendix A. (continued).

Experiment Snail Fish # of # of Mean # Mean # Type Habitat Species Size(mm) Number Size(mm) Trials Fish Eaten Attacked

Single size, multi-species, equal densities

VII. Sand LE 6 33 120 22 7 11.3 21.8 HA 6 33 150 32 8 18.6 26.8 CD 6 33

VIII. Sand PH 6 25 90 5 5 8.2 18.4 LE 6 25 120 5 5 9.8 12.8 HA 4.5 25 150 2 2 18.5 18.5 HA 6 25

Multi-size, multi-species, varying densities

IV.a. Sand AL 3 50 90 7 6 15.0 18.0 LE 3 50 120 9 9 17.9 24.3 150 6 6 20.0 22.0

IV.b. Sand AL 3 75 90 6 6 12.2 14.0 LE 3 25 120 8 8 20.4 28.8 150 8 7 25.4 26.9

IV.c. Sand AL 3 90 90 6 6 5.2 9.0 LE 3 10 120 8 8 10.6 17.0 150 7 7 27.7 30.4

IV.d. Sand AL 3 270 90 5 4 6.1 16.4 LE 3 10 120 8 8 7.8 14.4 150 7 7 37.7 40.4

V.a. Sand AL 3 50 120 9 9 14.8 21.9 LE 6 50 150 9 8 17.4 17.4

V.b. Sand AL 3 75 120 9 9 13.1 25.1 LE 6 25 150 8 8 14.0 14.1

V.c. Sand AL 3 90 120 9 9 6.7 11.9 LE 6 10 150 7 7 6.7 6.9

V.d. Sand AL 3 270 120 8 8 8.1 15.0 LE 6 10 150 8 8 10.4 10.6

X.a. Sand AL 3 50 150 8 8 8.5 13.3 LE 9 50 146 Appendix A. (continued).

Experiment Snail Fish # of # of Mean # Mean # Type Hahitat Species Size(mm) Number Size(mm) Trials Fish Eaten Attacked

Multi-size, multi-species, varying densities

X.b. Sand AL 3 75 150 8 8 12.4 16.0 LE 9 25

X.c. Sand AL 3 90 150 8 8 6.1 7.3 LE 9 10

X.d. Sand AL 3 270 150 8 8 7.9 10.1 LE 9 10 147 Appendix B.

If snails are randomly distributed with density D and fish have a 360° visual field, then the probability that at least one snail is found within its reactive distance L is

' ' I * - y* p = J 2nDxe~*Dx’ dx

= 1 _0e -*D(L*-y*)

And, if the visual field is 180° then the probability that at least one snail is within a fish’s reactive distance is

S L* - y* P~ j nDxe~0B*Dx'dx 0 ~ 1 - e~0BltD( L’ ~yt)

9