Citation
Predator-Mediated Coexistence and Multiple Predator Effects in a Treehole Community

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Title:
Predator-Mediated Coexistence and Multiple Predator Effects in a Treehole Community
Creator:
GRISWOLD, MARCUS W.
Copyright Date:
2008

Subjects

Subjects / Keywords:
Adulthood ( jstor )
Ecology ( jstor )
Entomology ( jstor )
Fish ( jstor )
Foraging ( jstor )
Larvae ( jstor )
Mathematical independent variables ( jstor )
Medical entomology ( jstor )
Predation ( jstor )
Predators ( jstor )
City of Vero Beach ( local )

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Marcus W. Griswold. Permission granted to University of Florida to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
4/30/2004
Resource Identifier:
55893181 ( OCLC )

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Full Text











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Figure 4-2. Mean survivorship (proportion of the original number of larvae surviving to adulthood) of 0. triseriatus (+ SE) at three
levels of resources and four levels of predation.











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Figure 4-3. Mean proportion of A. albopictus surviving (number of A. albopictus surviving divided by the number of A. albopictus +
0. triseriatus surviving) ( SE) at three levels of resources and four levels of predation.














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if


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Intermediate
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Figure 4-4. Mean combined survivorship of both prey species (Number of A. albopictus + 0. triseriatus surviving divided by total
prey originally present at start of the experiment) ( SE) at three levels of resources and four levels of predation.
















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Figure 4-5. Median time to adulthood of A. albopictus ( SE) at three levels of resources and four levels of predation.


















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Figure 4-7. Mean adult mass of male A. albopictus ( SE) at three levels of resources and four levels of predation.












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Figure 4-8. Mean adult mass of female A. albopictus ( SE) at three levels of resources and four levels of predation


on






























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A 2 C. appendiculata
04 C. appendiculata


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Figure 4-9. Mean adult mass of male 0. triseriatus ( SE) at three levels of resources and four levels of predation.











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A 2 C. appendiculata
04 C. appendiculata


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Figure 4-10. Mean adult mass of female 0. triseriatus ( SE) at three levels of resources and four levels of predation.
























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S1 C. appendiculata
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Figure 4-11. Mean estimates of population performance (X', an analog of the finite rate of increase for the cohort) (+ SE) for A.
albopictus at three levels of resources and four levels of predation. The line at X' = 1 is where the population is being
replaced, neither increasing nor decreasing.




Full Text

PAGE 1

PREDATOR-MEDIATED COEXISTENCE AND MULTIPLE PREDATOR EFFECTS IN A TREEHOLE COMMUNITY By MARCUS W. GRISWOLD A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

PAGE 2

Copyright 2004 by Marcus W. Griswold

PAGE 3

This thesis is dedicated to my parents, Jackie and Jerry, and to my wife, Ann. Their support and patience were undying and motivated me to the very end.

PAGE 4

ACKNOWLEDGMENTS I would like to thank my advisor, L.P. Lounibos, for his guidance and insight into my research as well as his many reviews of this manuscript. I benefited greatly from discussions with my committee, J. Stimac and R. Holt. I am grateful to J. Butler for graciously allowing me to use his lab space for my research. B. Bolker's patience and assistance with modelling contributed greatly to my understanding. S. Juliano and M. Capinu were of great assistance with SAS. I would also like to thank R. Escher for providing eggs of the prey species used in this research and P. Shirk and the USDA for the use of their microbalance. iv

PAGE 5

TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES...........................................................................................................ix ABSTRACT......................................................................................................................xii CHAPTER 1 INTRODUCTION AND REVIEW OF LITERATURE..............................................1 Mosquito Ecology Background....................................................................................1 General Ecology Background.......................................................................................9 2 FUNCTIONAL RESPONSE AND PREY VULNERABILITY................................13 Introduction.................................................................................................................13 Materials and Methods...............................................................................................15 Statistical Analysis......................................................................................................16 Results.........................................................................................................................18 Functional Response............................................................................................18 Prey Vulnerability...............................................................................................19 Discussion...................................................................................................................19 3 MULTIPLE PREDATOR EFFECTS.........................................................................30 Introduction.................................................................................................................30 Materials and Methods...............................................................................................31 Statistical Analysis......................................................................................................33 Model Selection...................................................................................................33 Data Analysis.......................................................................................................35 Results.........................................................................................................................36 Model Selection...................................................................................................36 Survival Analysis.................................................................................................38 Mean Days to Emergence....................................................................................39 Dry Mass.............................................................................................................39 Lambda................................................................................................................40 v

PAGE 6

Predator Survival.................................................................................................40 Discussion...................................................................................................................41 4 EFFECTS OF C. APPENDICULATA DENSITY AND RESOURCE QUANTITY ON PREY PERFORMANCE AND COEXISTENCE...............................................61 Introduction.................................................................................................................61 Materials and Methods...............................................................................................64 Data Analysis..............................................................................................................65 Results.........................................................................................................................66 Prey Performance................................................................................................66 Survivorship.................................................................................................66 Development time........................................................................................67 Adult mass....................................................................................................68 Lambda.........................................................................................................69 Predator Performance..........................................................................................69 Discussion...................................................................................................................69 5 EFFECTS OF HABITAT COMPLEXITY AND PREDATION ON PREY COEXISTENCE.......................................................................................................107 Introduction...............................................................................................................107 Materials and Methods.............................................................................................108 Data Analysis............................................................................................................109 Results.......................................................................................................................110 Discussion.................................................................................................................110 6 CONCLUSIONS......................................................................................................120 APPENDIX A R CODE FOR CHAPTER 3.....................................................................................123 B MULTIPLE COMPARISONS : CHAPTER 4.........................................................129 LIST OF REFERENCES.................................................................................................134 BIOGRAPHICAL SKETCH...........................................................................................152 vi

PAGE 7

LIST OF TABLES Table page 3-1 Parameter estimates for the combined predation model..........................................60 4-1 Two way ANOVA for , survivorship to adulthood, development time, and adult mass for A. albopictus..............................................................................................91 4-2 Results of Tukey tests of effects of Predator and Resource on A. albopictus survivorship.Mean values with a common underline are not significantly different for main effects (p < 0.05).........................................................................92 4-3 Two way ANOVA for , survivorship to adulthood, development time, and adult mass for O. triseriatus.....................................................................................93 4-4 Results of Tukey tests of effects of Predator and Resource on O. triseriatus survivorship..............................................................................................................94 4-5 Two way ANOVA for proportion of A. albopictus : O. triseriatus surviving and combined prey survivorship.....................................................................................95 4-6 Results of Tukey tests of effects of Predator and Resource on the proportion of A. albopictus surviving.................................................................................................96 4-7 Results of Tukey tests of effects of Predator and Resource on total prey survivorship..............................................................................................................97 4-8 Results of Tukey tests of effects of Predator and Resource on median time to adulthood for A. albopictus......................................................................................98 4-9 Results of Tukey tests of effects of Predator and Resource on median time to adulthood for O. triseriatus......................................................................................99 4-10 Results of Tukey tests of effects of Predator and Resource on female mass of A. albopictus adults.....................................................................................................100 4-11 Results of Tukey tests of effects of Predator and Resource on male mass of A. albopictus adults.....................................................................................................101 4-12 Results of Tukey tests of effects of Predator and Resource on female mass of O. triseriatus...............................................................................................................102 vii

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4-13 Results of Tukey tests of effects of Predator and Resource on male mass of O. triseriatus adults......................................................................................................103 4-14 Results of Tukey tests of effects of Predator and Resource on for A. albopictus .........................................................................................................104 4-15 Results of Tukey tests of effects of Predator and Resource on O. triseriatus . Mean values with a common underline are not significantly different for main effects (p < 0.05)....................................................................................................105 4-16 Two way ANOVA for , survivorship to adulthood, development time, and adult mass for C. appendiculata.............................................................................106 5-1 Two way ANOVA for survivorship to adulthood and median development time for A. albopictus.....................................................................................................118 5-2 Two way ANOVA for survivorship to adulthood and median development time for O. triseriatus.....................................................................................................119 B-1 Results of Tukey tests of effects of Predator and Resource on A. albopictus survivorship............................................................................................................129 B-2 Results of Tukey tests of effects of Predator and Resource on O. triseriatus survivorship............................................................................................................130 B-3 Results of Tukey tests of effects of Predator and Resource on male mass of O. triseriatus adults.....................................................................................................131 B-4 Results of Tukey tests of effects of Predator and Resource on the proportion of A. albopictus surviving...........................................................................................132 B-5 Results of Tukey tests of effects of Predator and Resource on total prey survivorship............................................................................................................133 viii

PAGE 9

LIST OF FIGURES Figure page 1-1 Fourth instar larvae...................................................................................................12 2-1 Functional response of C. appendiculata to A. albopictus.......................................24 2-2 Functional response of C. appendiculata to O. triseriatus......................................25 2-3 Functional response of T. rutilus to A. albopictus....................................................26 2-4 Functional response of T. rutilus to O. triseriatus...................................................27 2-5 Preference of C. appendiculata for A. albopictus indicated by Manly's alpha () (+/SD) versus proportion of A. albopictus available.............................................28 2-6 Preference of T. rutilus for A. albopictus indicated by Manly's alpha () (+/SD) versus proportion of A. albopictus available.............................................29 3-1 Daily per capita predation by T. rutilus as a function of predator:prey size ratio (determined by average instar).................................................................................49 3-2 Daily per capita predation by T. rutilus as a function of time (days).......................50 3-3 Daily per capita predation by C. appendiculata as a function of predator: prey size ratio (determined by average instar).................................................................51 3-4 Daily per capita predation by C. appendiculata as a function of time (days)..........52 3-5 Predicted and actual ( SE) per capita mortality of prey in combined predator treatments. Predicted values were calculated from the single predator treatments using the multiplicative risk model (Soluk and Collins 1988).................................53 3-6 Survival curves for survival of A. albopictus and O. triseriatus (combined)..........54 3-7 Mean survivorship (proportion of the original number of larvae surviving to adulthood) of A. albopictus and O. triseriatus ( SE)..............................................55 3-8 Mean time to adulthood for A. albopictus and O. triseriatus ( SE).......................56 3-9 Mean dry mass of male A. albopictus and O. triseriatus adults ( SE)...................57 ix

PAGE 10

3-10 Mean dry mass of female A. albopictus and O. triseriatus adults ( SE)................58 3-11 Mean estimates of population performance (', an analog of the finite rate of increase for the cohort) for A. albopictus and O. triseriatus adults ( SE)..............59 4-1 Mean survivorship (proportion of the original number of larvae surviving to adulthood) of A. albopictus ( SE) at three levels of resources and four levels of predation...................................................................................................................77 4-2. Mean survivorship (proportion of the original number of larvae surviving to adulthood) of O. triseriatus ( SE) at three levels of resources and four levels of predation...................................................................................................................78 4-3 Mean proportion of A. albopictus surviving (number of A. albopictus surviving divided by the number of A. albopictus + O. triseriatus surviving) ( SE) at three levels of resources and four levels of predation..............................................79 4-4 Mean combined survivorship of both prey species (Number of A. albopictus + O. triseriatus surviving divided by total prey originally present at start of the experiment) ( SE) at three levels of resources and four levels of predation..........80 4-5 Median time to adulthood of A. albopictus ( SE) at three levels of resources and four levels of predation............................................................................................81 4-6 Median time to adulthood of O. triseriatus ( SE) at three levels of resources and four levels of predation............................................................................................82 4-7 Mean adult mass of male A. albopictus ( SE) at three levels of resources and four levels of predation............................................................................................83 4-8 Mean adult mass of female A. albopictus ( SE) at three levels of resources and four levels of predation............................................................................................84 4-9 Mean adult mass of male O. triseriatus ( SE) at three levels of resources and four levels of predation............................................................................................85 4-10 Mean adult mass of female O. triseriatus ( SE) at three levels of resources and four levels of predation............................................................................................86 4-11 Mean estimates of population performance (', an analog of the finite rate of increase for the cohort) ( SE) for A. albopictus at three levels of resources and four levels of predation............................................................................................87 4-12 Mean estimates of population performance (', the composite index of fitness for the cohort) ( SE) for O. triseriatus at three levels of resources and four levels of predation...................................................................................................................88 x

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4-13 Mean development time to adulthood of C. appendiculata ( SE) at three levels of resources and four levels of predation.................................................................89 4-14 Mean adult mass of C. appendiculata ( SE) at three levels of resources and three levels of predation...........................................................................................90 5-1 Least square means ( SE) for survivorship of A. albopictus................................114 5-2 Least square means ( SE) for survivorship of O. triseriatus................................115 5-3 Least square means ( SE) for median development time of A. albopictus..........116 5-4 Least square means ( SE) for median development time of O. triseriatus..........117 xi

PAGE 12

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science PREDATOR-MEDIATED COEXISTENCE AND MULTIPLE PREDATOR EFFECTS IN A TREEHOLE COMMUNITY By Marcus W. Griswold May 2004 Chair: L. Philip Lounibos Major Department: Entomology and Nematology Direct and indirect effects of predators are known to influence interand intraspecific interactions among prey species and other predators. The effects of two species of predatory dipterous larvae on coexistence of their two common species of mosquito prey in south Florida treeholes were evaluated through controlled experiments. Functional response experiments with each single predator-single prey combination measured the responses of predators to prey density. Additional experiments determined relative prey vulnerability to a single predator species at a fixed density of the two prey species. Both Toxorhynchites rutilus and Corethrella appendiculata exhibited a Type II functional response to varying prey density. Both predators also preferred the invasive prey Aedes albopictus over the native species Ochlerotatus triseriatus, although the preference by T. rutilus was weaker. An experiment to determine the combined effects of the two predator species on coexistence of A. albopictus and O. triseriatus was conducted. Prey survivorship in the xii

PAGE 13

combined predator treatment was adequately predicted by single predator treatments using a multiplicative risk model, indicating additive effects. Intraguild predation occurred among predators in the combined predator treatment, but not until prey survivorship had been reduced by half. Predator species had different effects resulting from changes in size structure, with T. rutilus consuming more prey as time progressed and C. appendiculata consuming less. C. appendiculata regulated community structure by allowing both prey species to coexist while T. rutilus regulated abundance through intense predation. The effects of detrital resource density and C. appendiculata density on prey coexistence were evaluated in a controlled experiment. Increased levels of predation and resources resulted in enhanced coexistence between O. triseriatus and A. albopictus. Increased resources resulted in increased growth rates, survivorship, mass, and population performance for both prey species. Interference occurred among C. appendiculata and resulted in decreased development time of this predator. However, predators in high resource treatments emerged larger than those in low resources, suggesting the occurrence of a bottom-up cascade or alternative feeding method. The effects of habitat structure and predation by C. appendiculata on prey coexistence were evaluated. The presence of a predator, but not habitat structure, affected prey survivorship. As before, a greater number of A. albopictus survived in the treatments without predators, and a greater number of O. triseriatus survived in treatments with predators. The lack of an effect of increased structure on predation suggests that C. appendiculata is well adapted for finding prey in complex habitats. xiii

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CHAPTER 1 INTRODUCTION AND REVIEW OF LITERATURE Mosquito Ecology Background The introduction of invasive species into the U.S. and other parts of the world has become a common occurence (Sandlund et al. 1999 ). This may be even more problematic with the introduction of disease vectors such as mosquitoes by way of the used tire and exotic plant trade (Reviewed in Lounibos 2002). The establishment of an invasive species depends on a number of factors, including but not limited to the presence of effective natural enemies, competitors, environmental factors as well as the ability of the invasive species to find its own niche (Crooks and Soule 1999). The current study details the effects of abiotic and biotic factors on the ability of an invasive mosquito species to coexist with a native mosquito species. Corethrella appendiculata (Grabham) is a container-inhabiting Dipteran in the Family Corethrellidae (Borkent 1989), with a larval morphology and behavior similar to mosquitoes. This particular species has an extensive range in the Americas, from Argentina to latitude of 35 N (Stone 1968, Bradshaw and Holzapfel 1985). It has primarily been found in tree holes (Grabham 1906, Johnson 1979, Lounibos 1983), but is also found in artificial containers, such as tires (Morris and Robinson 1994, personal observation). In its larval stage, C. appendiculata is a facultative predator, feeding on smaller mosquito larvae and minute crustaceans (Grabham 1906, Lounibos 1985) and captures its prey by ambushing them, moving only the anterior part of the body towards the prey (Grabham 1906). In the absence of macroscopic prey, Corethrella spp. may 1

PAGE 15

2 feed on bacteria and protozoans (Lounibos 1985). C. appendiculata has four larval instars, of which only the third and fourth are large enough to consume mosquito larvae. Even as a third or fourth instar, this predator is able to consume only young mosquito larvae, no larger than 2 nd instars (Lounibos 1985). This size-dependent predation allows C. appendiculata a short window of time to consume mosquito larvae before they beome too large to capture. C. appendiculata is the smallest of the culicid or culicid-like container occupants in Florida (Fig. 1) (Lounibos 1983, Bradshaw and Holzapfel 1984). The larvae appear to spend most of their time on the bottom, or on the sides of the container, possibly secured by appendages on the terminal segment (Grabham 1906). However, quantitative behavioral studies have not yet been performed on this species. The second predator in this system, Toxorhynchites rutilus (Coq.), is a container-inhabiting mosquito, present throughout much of the eastern U.S. (Bradshaw and Holzapfel 1984). Species of the genus Toxorhynchites prey on aquatic or terrestrial invertebrates that have fallen into containers, and rarely vertebrates, of similar size or smaller, including culicids, chironomids, tipulids, ceratopogonids, psychodids, helodids, cladocerans, copepods, ostracods, oligochaetes, rotifers, protozoans, collembolans, thysanopterans, psocopterans, homopterans, hemipterans, hymenopterans, lepidopterans, arachnids, small tadpoles, and small dragonfly nymphs (Steffan and Evenhuis 1981, Campos and Lounibos 2000a). Although Campos and Lounibos (2000a) found that mosquito larvae were only minor components of the prey consumed by T. rutilus in treeholes and tires, they hypothesized that the container inhabiting mosquitoes may be consumed more often than other aquatic dipterans when abundant, especially Aedes albopictus (Skuse). In both tires and treeholes, T. rutilus had a significantly higher

PAGE 16

3 electivity coefficient for A. albopictus than would be seen at random. In tires this result was found for all T. rutilus instars while it was only found for fourth instar T. rutilus in treeholes (Campos and Lounibos 2000a). Jones (1993) also suggests that Toxorhynchites spp. larvae are well adapted for feeding on mosquito larvae. Toxorhynchites spp. larvae are primarily ambush predators (Steffan and Evenhuis 1981) though searching behavior may occur (Russo 1986, Linley and Darling 1993). The larva rests at a 45 degree angle with the surface of the water (Steffan and Evenhuis 1981) and moves to a horizontal position when ready to feed, attacking their prey laterally using both mandibles and coarse mouth brushes to secure prey (Breland 1949, Furumizo and Rudnick 1978). Russo (1986) found that more than 70 % of the prey captures occurred when the predator was not in contact with the water's surface. Depending on the location of the prey, Toxorhynchites spp. larvae may bend toward their prey or extend their head in order to reach their target (Linley 1990). The larvae may capture the prey at the surface or near the bottom of the container (Steffan and Evenhuis 1981) and may use mechanoreceptors to detect the prey, since compound eyes do not develop until the late pupal stage (Steffan and Evenhuis 1981). Lounibos et al. (1987) also found that Toxorhynchites spp. larvae will attack a probe that is vibrating at the appropriate frequency. Alternatively, some chemoreception may be involved in prey detection (Barber and Hirsch 1984). T. rutilus has been shown to have strong direct effects on prey abundance both in the field and in the laboratory (Bradshaw and Holzapfel 1983,Lounibos 1979, Focks et al. 1980,O’Flynn and Craig 1982,Hubbard et al. 1988,Yasuda 1996,Lounibos et al. 1997). Bradshaw and Holzapfel (1983) found in a study of north Florida treeholes that the presence of T. rutilus resulted in a significant reduction in pupal production of O.

PAGE 17

4 triseriatus, and resulted in local extinctions in some cases. Five times as many O. triseriatus larvae were present in treeholes without T. rutilus than those with T. rutilus (Bradshaw and Holzapfel 1984). However, a separate field study in south Florida found that although pupation success of O. triseriatus was reduced by T. rutilus, there were no correlations between O. triseriatus extinctions and the presence of T. rutilus (Lounibos et al. 1997). Lounibos et al. (1997) hypothesized that differences between the north and south Florida studies may have resulted from differences in sampling techniques, drought conditions or the inherent patchiness of treeholes. In field releases for biological control, T. rutilus has been shown to have strong negative effects on abundance of container inhabiting mosquitoes (Focks et al. 1980) Toxorhynchites spp. have also been found to decrease species richness, abundance, and affect prey size classes in tropical phytotelmata (Lounibos 1979,Lounibos et al. 1987). Thus, predation by Toxorhynchites spp. is important in determining community structure and possibly species coexistence. Cannibalism between Toxorhynchites larvae is common (Steffan and Evenhuis 1981) and is the most likely reason that only one or two larvae of this genus are usually found together in a container. The frequency of cannibalism is positively correlated with an increase in the density of predators (Steffan and Evenhuis 1981) due to an increased encounter rate and possible competition for prey. The presence of floating debris or increased detritus may allow the coexistence of more than one larva due to a decreased encounter rate (Lounibos 1979). Cannibalism among Toxorhynchites spp. larvae primarily occurs at low densities of alternative prey when small predators are consumed by larger conspecifics. (Yasuda and Hashimoto 1995). Toxorhynchites spp. larvae also fed on conspecific eggs in the laboratory (Linley and Duzak 1989) and in the field

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5 (Campos and Lounibos 2000b). In addition to feeding on smaller larvae of its own species, T. rutilus has been known to also feed on smaller larvae of C. appendiculata (Lounibos 1983). When T. rutilus and C. appendiculata of the same size are added to a single prey system,, the predators do not feed on each other in the presence of high densities of prey. However, once T. rutilus has matured to the 2 nd instar, it consumes fourth instars of the smaller predator, C. appendiculata (Lounibos 1985). In treeholes C. appendiculata are found in the diet of fourth instar T. rutilus more often than would occur at random. T. rutilus may have similar effects on C. appendiculata in artificial containers. The prey species, A. albopictus and Ochlerotatus triseriatus (Say), are similar in their behavior and biology (Jenkins and Carpenter 1946, Hawley 1988). Larvae of both species feed by filtering particles and by browsing on leaves (Walker and Merritt 1991, Leonard and Juliano 1995) and the sides of containers or treeholes. The adults lay desiccation-resistant eggs that hatch when immersed in deoxygenated water or rain, although a larger proportion of eggs of A. albopictus hatch during the first flooding than of O. triseriatus (Lounibos et al. 2001). The number of larvae initially hatching into a container can easily range into the hundreds. While many prey hatch out simultaneously, some eggs delay hatch, resulting in intermittent infusions of first instar larvae into the container (Livdahl and Koenekoop 1985). Ochlerotatus triseriatus is a native treehole species present throughout most of the eastern U.S. (Jenkins and Carpenter 1946, Bradshaw and Holzapfel 1985). In contrast, A. albopictus was introduced into the U.S. in 1985 in used tires from northern Asia (Sprenger and Wuithiranyagool 1986, Hawley et

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6 al. 1987, Reiter and Sprenger 1987) and has since spread throughout most of the southern and eastern U.S. (O’Meara et al. 1995, Moore 1999). Both prey species suffer from density-dependent intraspecific competition. Typically, high densities of larvae without an increase in resource base will result in longer development time, lower survival and smaller adults (Lord 1998), all of which may result in a decrease in the fitness of each species. However, when both species are together, A. albopictus outcompetes O. triseriatus via exploitative competition when fed both natural (oak leaves) (Barrera 1996) and non-natural food (liver powder and yeast) (Novak et al. 1993, Barrera 1996). However, when supplied with high levels of non-natural food, competitive effects were negated (Novak et al. 1993). A. albopictus was able to develop faster than O. triseriatus when alone and in a mixed culture, with A. albopictus consistently pupating before O. triseriatus. After 3.5 days most A. albopictus larvae were already 3 rd instars, while most O. triseriatus were only 2 nd instars. However, for O. triseriatus, the effects of intraspecific competition were greater than those of interspecific competition. O. triseriatus had a greater number of larvae surviving to adults, but a slower development rate when in a mixed culture versus being alone (Barrera 1996). It has been hypothesized that A. albopictus may develop faster than other container dwelling mosquitoes because it has a greater content of proteinases in its gut, which may lead to a more efficient and diverse feeding style (Ho et. al 1992). Therefore, if A. albopictus can competitively exclude O. triseriatus in its natural habitat, there is great potential for limiting the distribution of O. triseriatus. Although Livdahl and Willey (1991) predicted that competition between A. albopictus and O. triseriatus will lead to stable coexistence in treeholes and exclusion of O. triseriatus from tires in the

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7 absence of predators, T. rutilus and C. appendiculata may influence this predicted outcome. Laboratory and field experiments have manipulated various combinations of all four species, but never combined all of them in a single study. Since all four species co-occur in treeholes in the southeastern U.S., studies using all four may provide useful information on community interactions in this system. Previous research has shown that A. albopictus has a competitive advantage over O. triseriatus both in the presence and absence of T. rutilus (Ho et al. 1989, Novak et al. 1993, Teng and Apperson 2000, Lounibos et al. 2001). A few studies hypothesized that A. albopictus escapes competition and predation by having a faster development rate and hence emerging before the effects of competition and/or predation become detrimental to its survival (Novak et al. 1993, Teng and Apperson 2000, Lounibos et. al 2001). Predator selectivity or prey behavior may also be involved. In addition, although T. rutilus and C. appendiculata are known to be generalist predators, larval behavior of the predators and prey may affect how often the predators encounter each prey species. While manipulative experiments have been crucial to understanding the interactions between individual species, most of them have studied only T. rutilus as the predator, without addressing the combined consequences of a second predator, C. appendiculata. The dynamics that exist between multiple predators must be taken into account to reflect more accurately the natural environment. For instance, C. appendiculata may be able to influence survival of the prey species as well as the effects of T. rutilus on prey in several ways. First, it may reduce intraand interspecific competition among the prey species, allowing them to reach a size too large for predation by either predator.

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8 Alternatively, T. rutilus larvae have been found to prey selectively on larger prey larvae as they grow, consuming 3rd and 4th instar prey as they become larger (Bradshaw and Holzapfel 1983). This could leave behind younger instars to be fed upon by C. appendiculata, which can only consume 1st and 2nd instar mosquito larvae (Lounibos 1985). Secondly, C. appendiculata may compete with the prey species as well as consume them, by acting as an intraguild predator. The community interactions that occur will be especially important for the initial spring generation in the temperate range of these species and may set the tone for the entire breeding season. During the initial spring generation, treeholes may contain 3 rd and 4 th instar C. appendiculata around the same time that the summer generation of prey species such as O. triseriatus begin to hatch (Bradshaw and Holzapfel 1984). However, various stages of larvae may be present year round in subtropical regions such as south Florida, which may lead to more complex predator-prey interactions. In studies focusing on predation by C. appendiculata , varying densities of this predator initially affected the survival of the prey species, O. triseriatus, differentially over the first 24 hours, but the survival of the prey under different predator densities was similar thereafter (Lounibos 1983). This result probably occurred when prey reached a size that C. appendiculata could no longer consume. Although C. appendiculata may have only an initial effect on the prey species, this may enhance or interfere with the ability of a second predator. In a study by Lounibos (1985), C. appendiculata was consumed by T. rutilus after all of the O. triseriatus prey had been consumed. In this experiment, T. rutilus and the prey species, O. triseriatus were both introduced into the containers as 1 st instars. In the field, T.rutilus may not oviposit in a

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9 treehole until after its culicid prey have already hatched (Bradshaw and Holzapfel 1984). Although Nannini and Juliano (1997) found that this type of asynchronous development does not give O. triseriatus a significant head start to avoid predation by T. rutilus, the addition of a second prey species and a second predator could produce alternative outcomes. In a one predator, two prey system using T. rutilus as the predator, and O. triseriatus and Orthopodomyia signifera as the prey species, Chambers (1985) found that when the predator was present, O. signifera was released from the negative effects of competition with the other prey species due to differential predation on O. triseriatus. The addition of a second predator could lead to more complex predator-prey interactions. Predation by more than one predator can lead to a decrease (Wilbur 1972) or a discontinuance of competition (Morin 1981) within or between species. At different prey densities, T. rutilus and C. appendiculata may have an additive or non-additive effect on prey mortality. More specifically, at low prey densities, the predators may interfere with each other if the encounter rate between predators is similar to the encounter rate between predators and prey, resulting in more frequent cannibalism or interspecific predation. At higher prey densities, the encounter rate between predators may be lower than the encounter rate between predators and prey, leading to decreased predator mortality and increased prey mortality. In experiments that closely mimicked natural conditions, I observed the effects that both predators exerted on the survival, time and size at emergence of both prey species. General Ecology Background In nature, predation is an important factor in creating stable prey populations and structuring communities (Sih et al. 1998). Direct and indirect effects of predators can

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10 influence behavioral and demographic characteristics of prey. Predators can affect both prey habitat use (Murdoch and Oaten 1975, Mittlebach 1981, Rahel and Stein 1988, Werner and Hall 1988, Diehl and Eklov 1995) and activity levels (Sih 1984, Juliano and Reminger 1992) and in turn may influence length of larval period and size at emergence or metamorphosis (Wilbur 1987). Many studies in the past have examined single predator, single prey systems (Lounibos 1983, Wilbur et al. 1983), while more recent studies focus on interactions among multiple predators and multiple prey (Sih and Krupa 1996, Per Nystrom et al. 2001). The results of a single predator – single prey system are usually obvious: the predator will have a direct effect on the prey by killing it. Studies using a single predator may only be accurate if there is only one predator in the natural system or if the effects of a second predator on the prey species are additive, so that the predator species do not interact. Since many communities contain more than one predator species (Lima 1992, Soluk 1993, Sih and Krupa 1996), multiple predator experiments are of great value in ecological studies and will lead to more complex results. The most obvious of these are the possibilities of positive or negative interactions among predators and among prey. When two predators are present in a delimited community such as a treehole or container, they will most likely interact with each other, hence affecting community structure (Resetarits 1991, Sih and Krupa 1996). The predators may act synergistically (facilitation), causing a higher than expected prey mortality. This can occur when one predator causes the prey species to react differently, putting the prey at a higher risk of predation from the second predator. Alternatively, the predators may not interact at all so that the total mortality equals the mortality induced by the predators combined, which is

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11 referred to as additive mortality. If the total mortality from predators is less than the combined mortality due to each predator alone, it is referred to as non-additive mortality or interference (Ferguson and Stiling 1996). The primary objective of this project was to determine if two predator species interfere, are additive, or are facilitative when exposed to two competing prey species and how the predators interact to affect species coexistence. In order to accomplish this, a number of questions were asked. To evaluate predator-prey interactions, I examined both predator responses to prey and prey responses to predators. From known interactions in this system, I made the following predictions: (1) Both predators will show a consistent functional response with estimable attack constants and handling times, facilitating interspecific comparisons. I predict that both predators will have a higher maximum feeding rate on, and a preference for, A. albopictus because this species is a recent invader which has had less time to adapt to the predators than has the native O. triseriatus. (2) Increases in the density of C. appendiculata should result in an increase in the direct effect of predation on prey abundance. Since this species is found in higher numbers per container than T. rutilus, they may not interact at low densities and will lead to a direct increase in prey mortality. I also predict that changes in resource availability for the prey will be correlated with predator density so that increases in both resource density and predator density will allow for a higher proportion of O. triseriatus to coexist with A. albopictus. (3) Spatial heterogeneity and habitat complexity such as leaf litter will decrease the direct effects of predation. (4) Since both predators are primarily ambush predators, they will have limited interactions and will have additive effects when introduced into the system at the same size. (5) Over the time period of egg to pupa, T.

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12 rutilus will have a negative direct effect on C. appendiculata by consuming it. This type of predator-predator interaction will have a positive indirect effect on the prey species by reducing the number of predators and relaxing predation on both prey species Figure 1-1. Fourth instar larvae of (A) T. rutilus, (B) C. appendiculata, and (C) O.triseriatus. Scale = 0.67 mm. (Source: Lounibos, L.P. 1983 in Phytotelmata: Terrestrial plants as hosts for aquatic insect communities).

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CHAPTER 2 FUNCTIONAL RESPONSE AND PREY VULNERABILITY Introduction Predation is an important factor in structuring aquatic communities (Sih et al.1985) and may stabilize communities through density-dependent predation (Holling 1965, Hassell 1978) and switching (Murdoch 1969, Murdoch et al. 1975). Switching occurs when the most abundant prey type is chosen by the predator more often than it would be if chosen at random (Murdoch 1969, Murdoch et al. 1975). A first step in determining the effects of predation involves assessing predator behavior in relation to prey density and finding the maximum number of prey consumed by that predator. This may be especially important in small, temporary, aquatic communities such as treeholes and artificial containers where large numbers of mosquito larvae may hatch simultaneously and where predators and prey are limited in space. The functional response of a predator describes the behavioral response of the predator to varying densities of prey (Solomon 1949, Holling 1959a,b). Holling identified three primary types of functional responses, Type I, Type II, and Type III. Type I functional responses occur when prey consumption increases linearly in relation to prey density until it reaches a maximum; these are typical of filter-feeding invertebrates and occur when prey ingestion is proportional to encounter rate (except see Porter et al. 1983). Type II functional responses are inversely density dependent; as prey density increases, the proportion of prey killed decreases. Type III functional responses are density dependent until an inflection point is reached, whereupon the response curve 13

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14 approaches an asymptote as in Type II functional responses. Consequently, type III responses are represented by sigmoid-shaped curves and typically occur when the predator must learn to find its prey during the density-dependent portion of the curve and later resembles the Type II curve after the inflection point and prey are more easily found. All three types of functional response curves reach an asymptote when the predator is satiated, defined by the maximum number of prey that can be consumed during a fixed amount of time (Holling 1959a,b, Hassell 1978). Based on data from single predator – single prey systems, the relationship between predator and prey will be stable only when density-dependent predation occurs, as seen in type III functional responses (Murdoch and Oaten 1975). Calculations of attack constants and handling times from functional response experiments can be used to understand the mechanisms behind single predator – single prey systems, but they may not accurately predict events in systems where multiple prey species are present. Preference for a particular prey species can be predicted from estimates of the attack constant and handling time taken from functional response experiments using a single prey species (Cock 1978). Tests of predicted preferences can then be conducted by offering varying ratios of two prey species to the predator. Deviations from the predicted preference may occur due to changes in prey or predator behavior when two prey species are available. The objective of the first part of this chapter was to determine the type of the functional responses of T. rutilus and C. appendiculata and to estimate the associated parameters. Once the parameters are estimated, they can be compared among prey species to determine how the predator’s response to each prey species differs. The

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15 functional response curve will also predict the maximum feeding rate, or the maximum number of prey that can be consumed in a 24-hour period, for each prey species. I hypothesized that both predators would have a higher maximum feeding rate on A. albopictus and that A. albopictus would be preferred by the predators when O. triseriatus and A. albopictus are in the same microcosm. Materials and Methods Predator larvae and eggs were obtained from a laboratory colony maintained at 25 1C with an L: D (Light: Dark) cycle of 14:10 and relative humidity (RH) of ~ 70%. Larvae of C. appendiculata and T. rutilus were collected approximately every 6 weeks from a wooded site in Gainesville, FL, and added to the respective laboratory colonies to maintain genetic diversity. Two days before beginning the experiment, black oviposition cups were placed in the T. rutilus cage. One day before the start of the experiment, eggs of both prey species were hatched in deoxygenated water. Larvae of C. appendiculata were reared to 4th instars on a diet consisting of cultured nematodes (Lounibos unpublished). A. albopictus and O. triseriatus eggs from wild or F 1 females from south Florida were obtained from the Florida Medical Entomology Laboratory. To determine the functional response and the maximum number of prey species consumed by each predator species, predators were offered 12, 40, 80, 120, 200, or 250 first instar A. albopictus or O. triseriatus in 400 ml beakers with 400 ml dechlorinated tap water. T. rutilus was added as a first instar (less than 8 hrs old) and C. appendiculata was starved for 24 hours and was added as a larva that had molted to its fourth instar within 1-2 days prior to the start of the experiment. The treatments were randomly assigned to positions on a single shelf of the insectary and the predators were allowed to consume

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16 prey for 24 hours. After 24 hours, the predators were removed and the remaining prey were counted. Due to constraints on numbers of prey and predators available, replications were run at different periods under the same controlled conditions. More replicates were run at lower densities than at high densities (Juliano 2001). In a second experiment, first instar prey at a fixed density of 100 per 400 ml were offered to each predator at varying ratios of the two prey species. The predators were of the same instar as above. C. appendiculata was offered prey at ratios of A. albopictus: O. triseriatus of 0:100, 20:80, 40:60, 50:50, 60:40, 80:20 and 100:0, which were each replicated four times. T. rutilus was offered prey at ratios of A. albopictus: O. triseriatus of 0:100, 10:90, 30:70, 50:50, 70:30, 90:10, 100:0, each replicated six times. After 24 hours, predators were removed and remaining prey were identified to species and counted under a dissecting microscope. Statistical Analysis The shape of the functional response curve was determined by logistic regressions of the proportion of prey eaten as a function of the number of prey available (Trexler 1988). Since cubic models are of a high enough order to describe most curves (Juliano 2001), the following polynomial function was fit: N e = exp(P 0 + P 1 N 0 + P 2 N 0 2 + P 3 N 0 3) N 0 1 + exp(P 0 + P 1 N 0 + P 2 N 0 2 + P 3 N 0 3 ) with CATMOD (SAS 1989), where N e is the number of prey consumed, N 0 is the number of prey available and N e /N 0 is the probability that the prey will be eaten by a predator. The “P” values are parameters to be estimated using maximum likelihood methods. A type II functional response occurs when the linear term (or slope of N e /N 0 versus N 0 near N 0 = 0) is negative and a type III functional response occurs when the

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17 linear term is positive (Juliano 2001). The attack constants and handling times for each predator were estimated using nonlinear least squares regression (SAS procedure NLIN, SAS Institute Inc. 1989) on the random predator equation (Rogers 1972). To determine if the functional response parameters were significantly different between prey species for each predator, nonlinear least squares regression was used. To do this, each prey species was assigned an indicator variable and compared using the equation: 0 = N 0 N 0 exp{[a+D a (j)]{[T h + D Th (j)](N e ) T}} N e where j is the indicator variable with a value of 0 for O. triseriatus and a value of 1 for A. albopictus. Th is the handling time, a the attack constant, N0 the initial number of prey, and Ne is the number of prey consumed. Da and DTh are the differences in parameter estimates for the two prey species. Parameters were deemed to be significantly different when the 95 % confidence interval for the difference in parameters did not include zero. Significance was further tested with a t-test comparing the differences in parameter estimates to zero (Juliano 2001). Prey preference was also determined using Manly's (Manly 1974), modified by Chesson (1978,1983) for prey depletion = ]/)ln[(]/)ln[(]/)ln[TTTAAAAAANcNNcNNcN where N is the initial number and c is the number consumed of A. albopictus (“A”) and O. triseriatus (“T”). The predicted preference () for each predator was determined using attack constants from the functional response experiments.

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18 mjjiiaa1 where I is the predicted preference for prey type i with m types of prey and attack constants (a) estimated from the functional response. This equation can be used even when prey are not being replaced (Chesson 1983). Resulting values were compared among prey ratios by ANOVA to determine if preference is constant or changes with prey ratios. Each level was also tested against the predicted value with a t-test to determine if there was a significant preference for either prey species. Results Functional Response Logistic regressions showed that the linear term was negative for all predator – prey treatments, indicating Type II functional responses (Figs. 2-1, 2-2, 2-3, 2-4). These results were confirmed by plotting observed mean proportions consumed versus predicted proportions consumed. However, at the highest density of 250 prey, the number of A. albopictus consumed by both T. rutilus and C. appendiculata decreased from the 200 prey density (Figs. 2-1 and 2-3), indicating that predator behavior at very high prey densities may be influenced by a secondary factor(s). There were no significant differences among attack constants or handling times between the prey species when exposed to C. appendiculata. There were no significant differences in attack constants between prey species exposed to T. rutilus, but handling time was significantly greater for O. triseriatus than for A. albopictus (t = 3.87, p < 0.05, df =122). The average maximum feeding rate for C. appendiculata was ~24 prey/24 hours on A. albopictus (Fig. 2-1) and 18 prey/24 hours on O. triseriatus (Fig. 2-2). The

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19 average maximum feeding rate for T. rutilus was 21 prey/24 hours on A. albopictus and 14 prey/24 hours on O. triseriatus (Figs. 2-3 and 2-4). Prey Vulnerability Predicted preference of C. appendiculata for A. albopictus was slight with an alpha value of 0.52. When compared to an alpha level of 0.52 for no preference, C. appendiculata consistently consumed fewer O. triseriatus than predicted, preferring A. albopictus at all levels except when the ratio was 80:20 (A. albopictus : O. triseriatus) (Figure 2-5). However, ANOVA detected no significant variation in Manly's alpha among ratios of prey species with C. appendiculata (F = 0.98, p > .4, df = 4). Predicted preference of T. rutilus for A. albopictus was slight with an alpha value of 0.506. When compared to an alpha level of 0.506 for no preference T. rutilus only significantly preferred A. albopictus to O. triseriatus (Figure 2-6) when the ratio was 30:70. However, ANOVA detected no significant variation in Manly's alpha among ratios of prey species with T. rutilus (F = 0.34, p > 0.8, df = 4), indicating only a weak preference for A. albopictus. Discussion This study indicates that both predators exhibit a Type II functional response to changes in prey density, resulting from negative density dependence (Holling 1959a,b). A previous study found that both predators exhibited a Type II functional response to O. triseriatus, but the experiments were conducted in 30 ml containers and attack and handling parameters were not estimated (Lounibos 1985). The current study used 400 ml beakers, a volume that is more reflective of natural treeholes, which generally range from 40 ml to 1600 ml in south Florida (Lounibos 1983). Studies using other species of Toxorhynchites or other species of prey have also found that predators of this genus show

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20 a Type II functional response (Livdahl 1979, Russo 1983, Hubbard et al. 1988). The average maximum feeding rate of 14 prey in 24 hours is consistent with data from a study conducted by Lounibos (1985), which found that 1 st instar T. rutilus consumed ~ 14 O. triseriatus in 24 hours and with Russo (1983) who found T. rutilus to consume ~10 A. aegypti. However, other studies using 1st instar Toxorhynchites larvae found that this predator consumed an average of 3-5 prey per day when offered A. albopictus or Aedes aegypti (Hubbard et al. 1988, Furumizo and Rudnick 1978). Furumizo and Rudick held predator and prey in small containers with only 20 ml water and with a low number of prey (10:1 prey: predator ratio). The small number of prey consumed may have resulted from small container size and low prey density. Hubbard et. al (1988) used prey that were equal in size to T. rutilus and used second instar predators, possibly resulting in lower prey consumption. Since both predators reduce the number of prey consumed at the highest density of the current study when fed A. albopictus, predator confusion may be occurring (Pulliam and Caraco 1984). In other words, the prey may clump together to confuse the predator, similar to behavior of schooling fish (Neill and Cullen 1974). However, this density of 250 prey/400 ml is at least twice the density found in natural habitats for a similar species (Lounibos 1983, Bradshaw and Holzapfel 1984) suggesting that this phenomenon may seldom, if ever, occur in nature. The lack of any differences between the prey species in handling time, attack constant or maximum feeding rate when exposed to C. appendiculata suggests that this predator may react both prey species in an equal manner when the two are together. This is not surprising from the predator’s view since C. appendiculata is a generalist and captures its prey primarily by ambush (Grabham 1906). The lack of differences in

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21 functional response parameters might suggest that prey would be chosen equally when together. However, when the prey were exposed together C. appendiculata consistently chose A. albopictus over O. triseriatus. This suggests that prey behavior may affect feeding preference by this predator and that the presence of O. triseriatus may negatively affect A. albopictus survival in the presence of C. appendiculata. The ability of T. rutilus to consume a greater number of A. albopictus than O. triseriatus was attributable to the longer handling time required to consume O. triseriatus. However, the attack constant was found to be not significantly different from zero for either prey species. The results indicate that 1 st instar T. rutilus may consume a larger number of A. albopictus, but will reach its maximum feeding rate with fewer O. triseriatus larvae. If the prey species are of equal size, T. rutilus may gain more energy from O. triseriatus due to the fewer number of larvae required for this predator to become satiated. A similar but not significant trend was found for C. appendiculata, indicating that both predators may consume the invasive species more easily than the native prey species. Tests of prey nutritional quality are needed to determine if the prey types differ. C. appendiculata consumed a greater number of O. triseriatus and A. albopictus than did T. rutilus, consuming an average of 3 more larvae in 24 hours at the highest density. This finding conflicts with a study by Lounibos (1985), which found that T. rutilus consumed an average of 5 more O. triseriatus than did C. appendiculata. This discrepancy may lie in differences in container size, degree of predator starvation, predator size, or environmental conditions. In the current experiment, C. appendiculata larvae were larger than the T. rutilus larvae; a situation that may occur when T. rutilus larvae first colonize a container. T. rutilus larvae of a similar size to 4 th instar C.

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22 appendiculata would probably consume the same amount or more prey than C. appendiculata. Prey selection could be attributable to a number of mechanisms since prey behavior and predator behavior were not observed during the experiment. Although there were no differences in functional response parameters when C. appendiculata was given either prey species, the trend was for both predators to consume more A. albopictus larvae. Preference for A. albopictus could result from differences in encounter rates, attack frequencies and/or prey behavior all of which may change if prey are alone or together. Encounter rates with A. albopictus may have been greater than those for O. triseriatus since O. triseriatus reduces risky behaviors in the presence of T. rutilus but A. albopictus does not (Kesavaraju and Juliano 2004). The predicted preference indicated a very slight preference for A. albopictus , however the strong preference for A. albopictus is supported by the above statement. Thus, encounter rates may also depend on the spatial availability of the prey, since both predators are primarily ambush predators and wait to encounter prey. Lounibos et al.(1987) found that Toxorhynchites larvae living in tropical phytotelmata consumed more active prey such as Culicidae and Ceratopogonidae as opposed to more sedentary prey, such as Syrphidae, Psychodidae and Stratiomyidae. Behavioral studies on mosquito larvae have found that the position and activity of the prey larvae within a container affects the probability of predation by T. rutilus. Less active prey and prey that rest at the surface more often are less likely to be consumed by T. rutilus (Russo 1986, Juliano and Reminger 1992, Grill and Juliano 1996, Juliano and Gravel 2002). This may also hold true for C. appendiculata since it is also an ambush predator.

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23 Although this short-term experiment indicates that both predators have at least a slight preference for A. albopictus, longer term experiments (from hatch to adult) have not found differential selection of A. albopictus by T. rutilus (Lounibos et al. 2001). In the current experiment, when both prey species were available, T. rutilus had a weak, but significant preference for A. albopictus only when the ratio of A. albopictus to O. triseriatus was 30:70 and not when there was an equal mix of prey species. If this weak preference is consistent throughout the life of the predator, the competitive advantage that A. albopictus has over O. triseriatus (Ho et al. 1989, Livdahl and Willey 1991, Novak et al. 1993, Barrera 1996, Teng and Apperson 2000) may not be significantly lessened by this predator. Alternatively the weak preference exhibited by T. rutilus may have been a result of relatively low consumption rates in the first instar of this species. However, the consistent preference for A. albopictus by C. appendiculata may allow for coexistence of the prey species.

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rey consumed 30 25 (11) 20 (6) 24 Figure 2-1. Functional response of C. appendiculata to A. albopictus. Each point represents the mean number of prey consumed over a 24 hr period, with one SE above and below each point. a and T h are the parameter estimates ( 95 % CI). The solid line is predicted from the random predator model. Number of replicates are indicated in parentheses. 0 5 10 15 P (12) (12) (12) (12) (12) a = 0.13 ( 0.15) T h = 1.17 ( 0.14) 0 50 100 150 200 250 Prey available

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rey Consumed 30 25 (7) 20 25 Figure 2-2. Functional response of C. appendiculata to O. triseriatus. Each point represents the mean number of prey consumed over a 24 hr period, with one SE above and below each point. a and T h are the parameter estimates ( 95 % CI). The solid line is predicted from the random predator model. Number of replicates are indicated in parentheses. 0 5 10 15 P (5) (13) (15) (13) (13) (15) a =0 .12 ( 0.12) T h = 1.37 ( 0.16) 50 200 0 100 150 250 Prey Available

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30 25 (7) 20 (7) 26 Figure 2-3. Functional response of T. rutilus to A. albopictus. Each point represents the mean number of prey consumed over a 24 hr period, with one SE above and below each point. a and T h are the parameter estimates ( 95 % CI). The solid line is predicted from the random predator model. Number of replicates are indicated in parentheses 0 5 10 1Prey consumed (5) (10) 5 (7) (8) (11 ) a = 0.084 ( 0.07) Th = 1.29 ( 0.16) 0 50 100 150 200 250 Prey available

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rey consumed 30 25 1P 20 15 (9) (10) (13) (14) (9) (3) 27 0 (13) 5 a = 0.086 ( 0.06) T h = 1.77 ( 0.17) 0 50 200 0 100 150 250 Prey available Figure 2-4. Functional response of T. rutilus to O. triseriatus. Each point represents the mean number of prey consumed over a 24 hr period, with one SE above and below each point. a and T h are the parameter estimates ( 95 % CI). The solid line is predicted from the random predator model. Number of replicates are indicated in parentheses.

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00.10.20.30.40.50.60.70.80.9100.10.20.30.40.50.60.70.80.91Proportion A. albopictus in diet**** 28 Figure 2-5. Preference of C. appendiculata for A. albopictus indicated by Manly's alpha () (+/SD) versus proportion of A. albopictus available. Solid line indicates no preference for either prey species predicted from the functional response. Asterisks indicate significant difference from = 0.52 (p < 0.05).

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29 00.10.20.30.40.50.60.70.80.9100.10.20.30.40.50.60.70.80.91Proportion A. albopictus available* Figure 2-6. Preference of T. rutilus for A. albopictus indicated by Manly's alpha () (+/SD) versus proportion of A. albopictus available. Solid line indicates no preference for either prey species predicted from the functional response. Asterisks indicate significant difference from = 0.51 (p< 0.05)

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30 A number of studies have examined the role of a single predator species on prey dynamics (Focks et al.1980, Pastorok 1980, Russo 1983, Lounibos et al.1987, Blaustein et. al.1995, Yasuda 1996, Hechtel and Juliano 1997, Nannini and Juliano 1998, Kneitel and Miller 2002, Lounibos et al.2001), however, even some of the simplest communities may have multiple predator species (Fincke 1999, Kitching 2001). Originally it was thought that community structure with two predator species could be predicted from experiments with only single predator species, based upon additive effects of predation. However, non-additive effects of predators have been discovered recently to be common (e.g. Soluk 1993). Nonadditive effects of predators can be caused by both indirect and direct effects, including direct interference between predators (i.e. Intraguild Predation (IGP)(Polis et al.1989), changes in predator behavior (Huang and Sih 1991, Peckarsky and McIntosh 1998) and changes in prey behavior (Peckarsky and McIntosh 1998). Thus, studies incorporating multiple predators are crucial to understand direct and indirect interactions among predators and their prey Historically, predator species have typically been grouped into guilds or functional groups based on their trophic level (Oksanen et al.1981, Menge and Sutherland 1987, Schmitz 1992). However, recent work has shown that different predator species may have differential effects on the same prey assemblage and thus should not be grouped together CHAPTER 3 MULTIPLE PREDATOR EFFECTS Introduction

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31 into a single unit. Predator species may differentially affect prey behavior, survival and development (McPeek 1998, McIntosh and Peckarsky 1999, Relyea 2001, Schmitz and Suttle 2002, Kelly et al.2002, Schmitz and Sokol-Hessner 2002). The effects of individual predator species may be especially important as predator and prey change in size (Travis et al.1985, Fauth and Resetarits 1991, Van Buskirk 1988, Wissinger 1988). Studying the effects of individual predator species over a single predator and prey generation will be useful for determining the mechanisms behind the predator-prey and predator-predator interactions. Treeholes are relatively simple communities in that they typically only contain a few predator species (Kitching 2000). In south Florida, two commonly found predator species, C. appendiculata and T. rutilus, may co-occur within the same container (Lounibos 1983). In addition, relatively large numbers of C. appendiculata may be found together in the same container (Lounibos 1983, Morris and Robinson 1994). As containers are invaded by prey species such as A. albopictus and O. triseriatus, the predators may interact in a number of ways to differentially affect prey survival and coexistence among prey species. Since T. rutilus is the top predator in this system and has the ability to affect the intermediate predator C. appendiculata, as well as the basal prey species IGP is probably important in this system. Although both predator species are in the same Order, other studies have found that prey behavior and thus prey consumption is dependent upon species-specific characteristics of the predator (Turner et al.1999, Schmitz and Suttle 2001). Materials and Methods A semi-natural experiment was set up to examine the effects of multiple predators on prey survival and growth. Tire water was collected and sieved through a 180 m filter

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32 to remove macroinvertebrates, including their eggs. Water from various locations was pooled and mixed before being allocated to treatments. Twenty-eight 500 ml black plastic containers were used as experimental microcosms for the predator and prey species. Each container received 350 ml of tire water and 2 g of chopped oak leaves (Quercus virginiana) that were dried at 65 C for 48 hours. The containers were covered with screen (mesh ~ .76 mm) to prevent any immigration into the system, but allowing rainfall to enter. A single hole above the water line was punched in the side of each container and covered with nylon mesh (~ 210 m), to allow for any overflow and to prevent loss of small larvae. Deionized water was added daily as needed to maintain 350 ml of water in each container. The containers were placed in the Natural Area Teaching Lab for three days before the beginning of the experiment to allow microorganisms to accumulate on the leaf litter and sides of the containers. The containers were mounted on stakes ~ 1 m above the ground to limit disruptions by foraging animals. A HOBO datalogger was placed at the site to monitor daily temperature and humidity. Average temperature at the site was 25.5C ( 2.79 SD) from June 6, 2003 to July 30, 2003. The experiment was setup as a completely randomized design manipulating the presence or absence of each predator species. Each treatment received 50 1 st instar A. albopictus and 50 1 st instar O. triseriatus. The treatments consisted of no predator, one 1 st instar T. rutilus, two 4 th instar C. appendiculata, or one 1 st instar T. rutilus and two 4 th instar C. appendiculata. Forty-eight hours before the experiment, fourth instar C. appendiculata larvae that had metamorphosed from 3rd to 4th instar within the previous 24 h were given aquatic nematodes ad libitum and then were starved for 24 hours before the experiment began.

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33 Containers were brought back to the lab and censused daily, with prey identified to species using a dissecting microscope. At each census period, the number of surviving larvae of each species as well as larval instar of the predators and prey were recorded, the latter determined by comparing head capsule widths. Prey larvae were left in the containers until they emerged as adults which were aspirated from the container and frozen. When the experiment was concluded by the final emergence thawed adults were dried at 65 C for 48 hours and subsequently weighed to the nearest 0.001 mg on a microbalance (Mettler, Switzerland). Statistical Analysis Model Selection Statistical models force one to make the data fit to a specific model. This may be difficult, especially when data do not follow the typical normal distribution required for many statistical tests. One alternative to purely statistical models are ecological models, where the data are fit to the model. Data in this case do not necessarily need to follow a normal distribution and the model may be based upon another distribution such as the binomial. Another advantage of ecological models is that they are useful for looking at the data mechanistically and thus determining biological significance. Programs were created and run in R (R Development Core Team 2003). Since the prey were either consumed or not consumed, an underlying binomial distribution was assumed. For each predator treatment, two potentially independent factors were considered to affect daily consumption rates: days elapsed since predator and prey were added and the predatorprey size ratio. For each predator treatment, a mathematical function was chosen to describe the plots of both days elapsed since the experiment began and predatorprey size ratio as a function of per capita prey mortality. The

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34 mathematical function was determined by visually inspecting plots of per capita predation versus day and versus predator: prey size ratio. This mathematical function was then used as the probability parameter in the binomial distribution. Each model was tested over a range of values to find the best parameters for the data. The minimum value of the negative log likelihood function was calculated for each group of parameters to determine how well the model fit. Confidence intervals were calculated for each parameter. Akaike Information Criterion (AIC) was used to determine if the day or size ratio models fit the data significantly differently. The AIC calculation is: A i = L(Y|M i ) + 2p i Where M i is the model, p i are the parameters and L(Y|M i ) is the negative log-likelihood, where Y is the data (Hilborn and Mangel 1997). Models are statistically significant if their AIC values differ by 2 or more (Sakamoto et al.1986). The models for the single predator treatments were also compared to each other using AIC. The models from the single predator treatments were then used to determine if the two-predator treatment yielded similar results as would be predicted from the combination of the two single predator models. A multiplicative risk model (Soluk and Collins 1988) was used to predict the combined survivorship of prey from single predator treatments: C ct = n p D(p c + p t – p c p t ) Where C is the predicted number of prey consumed in the combined predator treatment; n p is the total number of prey; p is the probability of being consumed by C. appendiculata (c) or by T. rutilus (t). D is an added parameter that will indicate additivity or non-additivity of the predators on prey mortality. The model is additive when D=1, and non-additive when D is greater (interference) or less (synergistic) than 1.

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35 This model is more useful than an additive model of prey consumption because it does not allow an individual prey item to be consumed twice like the additive model does (Soluk and Collins 1988). The multiplicative model, which assumes no predator interactions, was fit to the data and was compared with the model allowing interference or synergistic predator interactions. Since these models were nested, negative log likelihood values were compared using a chi-squared test with degrees of freedom equal to the difference in the number of parameters between models, equivalent to the Likelihood ratio test (Hilborn and Mangel 1997). Data Analysis Mean development time, survivorship (proportion surviving) and dry mass for each prey species were analyzed by one-way ANOVA using PROC GLM in SAS (SAS Institute 1989). Some data did not meet assumptions of normality and/or homogeneity of variance and were thus transformed. A. albopictus and O. triseriatus survivorship were arcsine transformed. A. albopictus development time was reciprocal transformed. When significant main effects were found, all pairwise comparisons were made using the Tukey-Kramer method (SAS Institute 1989). Since data were censored (e.g. when prey died or emerged), a log-rank test for homogeneity of the survival curves was run using PROC LIFETEST in SAS. Survivorship, development time and dry mass were used to calculate a composite index of performance,, similar to the finite rate of increase defined in Juliano(1998):

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36 xxxxxxwfxAwfAN)()()/1(lnexp0 xxxwfAD)( where N 0 is the initial number of females ( assumed to be 50% of a cohort), A x is the number of females eclosing on day x, w x is the mean dry mass of females eclosing on day x, and f(w x ) is a function relating egg production to dry mass. D is the time from adult eclosion to reproduction, taken as 12 d for O. triseriatus (Leonard and Juliano 1995, Grill and Juliano 1996) and 14 d for A. albopictus (Livdahl and Willey 1991). A regression relating adult dry mass to fecundity for A. albopictus was obtained from Lounibos et al.(2002): f(w x ) = 19.5 + 152.7 w x (r 2 = 0.573) and for O. triseriatus from Nannini and Juliano(1997) : f(w x ) = (1/2)exp[4.5801 + 0.8926(ln w x )] – 1 (r 2 = 0.5377 ) Values of greater than 1 indicate that the population is increasing, 1 that the population is remaining the same, and less than 1 that the population is decreasing. Results Model Selection For treatments with only T. rutilus, a three parameter exponential function was fit to the data for size ratio: exp(K-(ratio*L)) (Fig. 3-1). A two-parameter logistic function was fit to the data for days elapsed (1/(H+exp(-I*(day-J)))) (Fig. 3-2). Residuals of day plotted against size ratio and of size ratio plotted against day show a similar scatter for both plots. AIC values were 256.5 for the day model, and 298.1 for the ratio model.

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37 Since models with differences in AIC values greater than 2 are considered significantly different (Sakamoto et al.1986), the day model is a considerably better fit than the ratio model. For treatments with only C. appendiculata, a two-parameter logistic function was fit to the data for size ratio (1/(1+exp(-B*(ratio-A)))) (Fig. 3-3). A two parameter hyperbolic function was fit to data for day (E/(day + F)) (Fig. 3-4). Residuals of day plotted against size ratio and of size ratio plotted against day showed dissimilar scatter plots. AIC values were 211.9 for the ratio model, and 128.4 for the day model. Thus, the day model was a significantly better fit than the ratio model. Plots of per capita predation versus day for treatments with both predators did not show a pattern, so a mathematical model was not fit to these data alone. Data from each single predator treatment fit the expected data for combined effects well using the multiplicative risk model: D (P 1 + P 2 (P 1 *P 2 )) where P is the proportion of prey consumed by each predator. The model setting D=1 and allowing the parameters to vary was the best fit to the data (Table 3-1). The best parameters for the single predator models varied when used for the combined predator treatments when D is equal to one (additive model), indicating some change in predator effects from the single predator treatments to the combined treatment (Table 3-1). The multiplicative model did not differ significantly from the true model (Fig 3-5). Although the data indicated interference among the predators, confidence intervals were too large to be significantly different from the multiplicative risk model.

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38 Plots of daily temperature versus per capita predation did not show any obvious pattern. The data were highly scattered and the average daily temperature only ranged from 22 – 27 C . Survival Analysis Survival curves (Fig. 3-6) were compared among predator treatments using PROC LIFETEST in SAS (SAS 1989). Both prey species were pooled together as one species since the pupae were difficult to differentiate and are preferentially consumed by a related species of Toxorhynchites (Lounibos 1979). A log-rank test of homogeneity of the survival curves indicated that survival curves were significantly heterogeneous ( 2 = 276.38, p<0.001). Multiple comparisons indicated that curves for C. appendiculata alone were significantly different from those for T. rutilus alone (z = 11.6128, p < 0.001) and the predators together (z = 16.365, p < 0.001), and that T. rutilus alone was significantly different from the treatment with both predators (z = 3.50989, p < 0.001). Median survival times for prey exposed to C. appendiculata alone, T. rutilus alone, and the predators together were 9.68 d, 8.26d and 4.28 d respectively. There was a significant effect of predator on A. albopictus mean survivorship (F 3,23 = 19.16, p < 0.0001). Mean survivorship was significantly greater for the treatment without predators than those with predators. Mean survivorship was significantly greater for A. albopictus exposed only to C. appendiculata compared to those exposed to T. rutilus. There was no significant difference between mean survivorship when exposed to T. rutilus alone and T. rutilus with C. appendiculata (Fig 3-7). There was a significant effect of predator on O. triseriatus mean survivorship (F 3,23 = 18.50, p < 0.001). Mean survivorship was significantly greater when exposed to C.

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39 appendiculata alone than any other treatment, including the treatment with no predator. Mean survivorship with C. appendiculata was twice as high as when no predator was present. There was no significant difference in mean survivorship between exposure to T. rutilus alone and exposure to T. rutilus and C. appendiculata together (Fig 3-7). Mean Days to Emergence There were no significant effects of predator treatment on mean time to emergence for O. triseriatus (F 3,19 = 2.41, p ~ 0.098)(Fig. 3-8). There was a significant predator effect on mean time to emergence for A. albopictus (F 3,22 = 56.27, p < 0.0001). All of the predator treatments resulted in significantly less mean time to emergence than the treatments without predators. Prey exposed to treatments with T. rutilus took significantly less time to complete development than those with C. appendiculata (Fig. 3-8). Dry Mass Predators did not have a significant effect on mean mass of A. albopictus adult males (F 3,21 = 3.05, p ~ 0.051)(Fig. 3-9). There were significant effects of predators on mean mass of A. albopictus adult females (F 3,14 = 9.92, p < 0.001). Females emerging from treatments with C. appendiculata emerged significantly larger in mean mass than those in treatments without predators (Fig 3-10). There were significant effects of predators on mean mass of O. triseriatus adult males (F 3,12 = 6.98, p < 0.006). Males in treatments with T. rutilus emerged significantly larger in mean mass than those in treatments without predators (Fig 3-9). There were significant effects of predator on mean mass of O. triseriatus adult females (F 3,10 = 14.48, p < 0.001). Females in treatments with T. rutilus emerged significantly larger in mean mass than those in treatments without T. rutilus (Fig. 3-10).

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40 Lambda There was a significant effect of predators on mean values of lambda for A. albopictus (F 3,23 = 12.54, p < 0.0001). Mean lambda values for A. albopictus exposed to C. appendiculata or no predator were significantly greater than those for prey of this species exposed to T. rutilus alone and both predators (Fig 3-11). There was a significant effect of predators on mean values of lambda for O. triseriatus (F 3,23 = 4.73, p < 0.02). Mean lambda values for O. triseriatus with C. appendiculata were significantly greater than for all other treatments (Fig. 3-11). Predator Survival In treatments with only C. appendiculata, 86 % of the C. appendiculata larvae survived to adulthood (2 died as pupae), while in treatments with C. appendiculata and T. rutilus, none of the C. appendiculata larvae survived to adulthood. Thus, mean survivorship of C. appendiculata was significantly greater in treatments without T. rutilus (t = 9.3, df=6, p < 0.0001). When T. rutilus was present, C. appendiculata survived an average of 5.17 (0.87 SE, n = 12) days. At this point the average instar of T. rutilus was 2.75 ( 0.28 SE, n = 6) and ~ 40 % of the mosquito prey remained. Although none of the T. rutilus larvae died, a few were not able to pupate because the number of prey available were insufficient for completion of development. In treatments without C. appendiculata, six out of the seven T. rutilus larvae pupated, while in the treatments with C. appendiculata four out of the six T. rutilus larvae pupated. T. rutilus in treatments without C. appendiculata took an average of 18.67 0.49 (SE) days to pupate, while those in treatments with C. appendiculata took an average of 20.25 0.63 (SE) days to

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41 pupate. There was no significant difference in mean time to pupation for T. rutilus between treatments with and without C. appendiculata (t = 1.98, p = 0.095, df = 6). Discussion As seen in a number of experiments, the effects of multiple predators on prey survival were additive (Buskirk 1988, Fauth and Resetarits 1991, Beachy 1994, Dinter 2002). This was surprising given that intraguild predation occurred between T. rutilus and C. appendiculata. The results indicate that in the situation when prey colonize a container under the above conditions, initial predation rates are highly dictated by C. appendiculata. However, once the prey become too large to be consumed by C. appendiculata, predation rate is primarily regulated by T. rutilus. This is supported by the number of adults of the prey species emerging being similar in treatments with T. rutilus alone and both predators together. Under the conditions studied, predator species differed in effects, and these effects were predictable from single predator treatments. As in other studies (Relyea 2001, Schmitz and Suttle 2001), the two predator species in this study had separate overall effects on the prey species. Prey species may exhibit different antipredator behaviors for each predator species (Peckarsky and McIntosh 1998). For O. triseriatus predation by T. rutilus was approximately equivalent to the effects of competition. C. appendiculata alone allowed equal numbers of both prey species to survive, potentially leading to coexistence between the prey species. A predator can allow for coexistence of two competing prey species if the superior resource competitor is more affected by predation (Holt et al.1994), which is the case with predation by C. appendiculata on A. albopictus. However, anytime T. rutilus was present, prey survivorship was drastically reduced and C. appendiculata could not counteract the strong effects of T. rutilus. Development of A. albopictus took

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42 significantly longer in treatments without predators than those with. This indicates that both predator species reduced competition enough to allow prey to develop faster (Nystrom et al.2001, Morin 1983). Female A. albopictus adults were larger in treatments with C. appendiculata present, supporting the above statement. However, mass of female A. albopictus adults in treatments with only T. rutilus were not different from those in treatments without predators. Thus, to A. albopictus risk of predation by T. rutilus is greater than that from C. appendiculata, since adults took longer to emerge and emerged larger in treatments with C. appendiculata alone than those with T. rutilus. In this experiment, A. albopictus could no longer be consumed by C. appendiculata after ~ 5-6 days since the prey becomes too large to be consumed by this predator. When this prey species is exposed to T. rutilus, the risk of predation increases with time since the predator grows larger with time. Although development time of O. triseriatus was not affected by predator treatment, mass was greater for adults that emerged from treatments with only T. rutilus. To a greater extent than C. appendiculata, T. rutilus may reduce prey density and thus competition among prey and allow larger O. triseriatus adults. Regardless of treatment, lambda values were never greater than one for O. triseriatus, indicating that the population of this species would always decrease in the presence of A. albopictus. However there was a trend towards higher values of lambda for this species when exposed to C. appendiculata, indicating that this predator has potential for fostering coexistence among the two prey species. C. appendiculata is especially beneficial to O. triseriatus, countering the effects of exploitative competition with A. albopictus. The presence of T. rutilus is detrimental to both prey species and eliminated C. appendiculata once the basal prey density became low. Thus, even in the

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43 presence of 4th instar C. appendiculata, colonization of a container by T. rutilus may negatively affect the prey species and C. appendiculata. In particular, asymmetric intraguild predation (IGP) may be observed in these results. Asymmetric IGP occurs when a predator species, A, consumes predator species B, but predator B does not consume predator A (Polis et al.1989). Asymmetric IGP is common among other freshwater insects (Merrill and Johnson 1984, Peckarsky 1982) and has been found to occur among both vertebrate and invertebrate predators inhabiting treehole communities (Fincke 1999). Predators are more likely to have non-additive effects on prey when asymmetric IGP is present (Hurd and Eisenberg 1990, Hurd et al.1983). In the current experiment, the effects of C. appendiculata on prey survival are strong during the first few days, when the effects of T. rutilus are somewhat weak. A short period of prey vulnerability has been hypothesized to be the result of additive effects of two predators in other aquatic systems. Buskirk (1988) found additive effects of multiple predators even in the presence of asymmetrical IGP. He hypothesized that most predation occurred early on in the experiment before IGP could occur and effect prey survival. Fauth and Resetarits (1991) also found simple additive effects of predators when the prey were only vulnerable to the intermediate predator for a short period of time. The initial starting conditions of this experiment simulate a situation where C. appendiculata are already in a container and the other three species hatch as larvae in the container. However, this is only one of a number of sequences whereby these species could colonize this aquatic system. Thus, it is important to understand the effects of multiple predators through time.

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44 For the IG predator, the IG prey, and the basal resource to coexist, certain criteria must be met: (1) the IG prey must be a superior resource competitor when compared to the IG predator, (2) the IG predator must not consume IG prey often, or (3) the IG predator gains more energetic value from the basal prey than from the IG prey (Polis et al.1989, Polis and Holt 1992, Holt and Polis 1997). Even when T. rutilus and C. appendiculata were similar in size, they consumed approximately the same number of prey. When T. rutilus grew larger than C. appendiculata, it could consume more and larger prey than C. appendiculata. As suggested by Lounibos (1985), the predators may be equivalent competitors when they are a similar size. T. rutilus consumed C. appendiculata after only approximately half the basal resource had been consumed, suggesting that the IG predator will often consume the IG prey. It is not apparent from this experiment whether T. rutilus derives more energy from C. appendiculata or from the basal prey. T. rutilus did not develop faster or emerge larger in treatments with C. appendiculata compared to those without, however prey depletion by C. appendiculata may have confounded any effects. Thus, T. rutilus and C. appendiculata may not be able to coexist, and C. appendiculata may be excluded under the conditions studied. Size structure is another important component of IGP. T. rutilus is a generalist predator (Campos and Lounibos 2000b) that will grow much larger than C. appendiculata, and is highly cannibalistic on smaller conspecifics (Campos and Lounibos 2000b). All of the above are general features found in other systems with size structured IGP (Polis 1981, Reaka 1987). Interactions among predators may change as predators change in size. When each predator is larger than the gape of the other, interactions should be limited to simple competition (Fauth and Resetarits 1991). This size-structured

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45 interaction was especially important in this experiment when T. rutilus did not consume C. appendiculata until the former was a 2nd or 3rd instar. Lounibos (1985) found similar results in the absence of alternative prey with T. rutilus only consuming C. appendiculata after it molted to its second instar. However, IGP could have also occurred because of decreased densities of alternative prey (Hodskin 1971, Lonsdale et al.1979, Yen 1983). Competition between the predators may have increased and it would be a useful adaptation for T. rutilus to eliminate competition, while gaining energy through the consumption of C. appendiculata. IGP can have a stabilizing effect on the top predator when the basal resource is diminished, but may have a destabilizing effect on the IG prey due to increased mortality from the IG predator (Polis et al.1989). IGP in other temporary systems has been found to increase the growth rate of IG predators in amphibian larvae (Wilbur 1988). However in this system, T. rutilus did not pupate faster in treatments with C. appendiculata than in treatments without C. appendiculata. This may have been due to prey depletion in the combined predator treatment since C. appendiculata significantly reduced prey abundance before being consumed. Also, only two C. appendiculata larvae were used in treatments with combined predators, which may not have been enough to significantly effect development of T. rutilus. However, increased numbers of C. appendiculata may lead to extinction of the basal prey which would be even more detrimental to first instar T. rutilus colonizing a container. Another explanation is that many of the prey were invulnerable to C. appendiculata when it was consumed by T. rutilus. C. appendiculata may have needed to move around more in order to find vulnerable prey and thus may have encountered T. rutilus more often. Other freshwater insects also exhibit

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46 asymmetrical, size structured IGP (Benke 1978, Folsom and Collins 1984, Robinson 1987). Rapid growth can be a defense against gape limited predators, where the inferior competitor will be exposed to the predator longer than the faster growing competitor (Wilbur et al.1983). However, predators like T. rutilus that preferentially consume larger prey (Lounibos 1979) will have strong effects on the upper end of prey size distribution and prey differentially on the superior, faster growing competitors (Wilbur et al.1983). When both predators are present it is a difficult situation for mosquito prey, slow growing prey may be consumed quickly by both C. appendiculata and T. rutilus, while fast growing prey may be consumed by T. rutilus. Although interactions among predators were not significant, behavioral interference among predators may have occurred before IGP occurred between the predator species. This could have resulted from interaction modifications, where one predator affected the type of direct interaction between the second predator and its prey (Wooten 1994, Sih et al.1998). Since T. rutilus consumes C. appendiculata when it grows large enough, C. appendiculata may avoid interactions with this predator, which may reduce predation intensity from C. appendiculata. Alternately, the predator species may have interacted directly through aggression. Starved C. appendiculata larvae will fight over a single prey item even when there is other prey available, but will not consume similar sized conspecifics (personal observation). Sih et al.(1998) hypothesized that two ambush predators should rarely encounter each other and should be weak interferers. This was not the case in this experiment and others using predator species found in treeholes (Corbet 1985, Lounibos et al.1996, Campos and Lounibos 2000a). The relatively small

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47 habitat of treehole systems and the ability to switch foraging modes from ambush predation to active foraging in T. rutilus (Linley and Darling 1993) may lead to predator-predator interactions. T. rutilus commonly interacts with and consumes conspecifics (Campos and Lounibos 2000b), so it is not surprising that it would readily consume another species of predator. Even when T. rutilus was the same size or slightly smaller than C. appendiculata, C. appendiculata never consumed T. rutilus. The same result occurred when a single T. rutilus larva was added to a container with numerous starved C. appendiculata larvae (personal observation). C. appendiculata did not even attempt to strike T. rutilus larvae that were within range, suggesting behavioral avoidance regardless of how large T. rutilus is. Further studies are needed to determine if C. appendiculata avoids consuming T. rutilus because of behavioral avoidance or if T. rutilus in not palatable for C. appendiculata. It is particularly important to examine the effects of individual predator species, since each predator species may differentially affect prey survival (McPeek 1998, McIntosh and Peckarsky 1999, Relyea 2001, Schmitz and Suttle 2002, Kelly et al.2002, Schmitz and Sokol-Hessner 2002). In treehole environments, T. rutilus is thought to be a keystone predator (Kitching 2000). However in this short term experiment C. appendiculata seemed to regulate species composition, while T. rutilus regulated abundance. Similar results have been found in some freshwater systems with amphibians, where the top predator regulated total abundance and the intermediate predator acted as a keystone predator, influencing species composition (Morin 1981, Morin 1983, Fauth and Resetarits 1991). A keystone predator is also thought to have strong effects on prey populations regardless of the presence of other predators (Paine 1966, Navarette and

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48 Menge 1996). This was the case for T. rutilus, where it severely depressed prey abundance in treatments with and without C. appendiculata. However, a small, abundant predator with small per capita effects, but large overall impact can be as important in a community as a large, less abundant predator with large per capita effects, such as T. rutilus (Barkai and McQuaid 1988). Although only two C. appendiculata larvae were used in this experiment, as many as ten to fifteen larvae may be found together in a container (Lounibos 1983, Morris and Robinson 1994, personal observation) and four or more larvae may result in prey extinction under the conditions studied (unpublished). Lounibos (1983) proposed that a significant negative association between C. appendiculata and O. triseriatus in treeholes was in part due to predation by C. appendiculata, corroborated by an overall negative relationship between abundances of the two species in sampled holes This indicates that both predator species are important, especially in the absence of the other. Although studies focusing on the effects of multiple predators are now commonplace in the literature, only a few studies have been conducted in phytotelmata. Since predator guilds in these systems typically consist of only a few predator species, community manipulations are rather simple as compared to other large-scale aquatic systems. Futhermore, studies in phytotelmata may be useful for forming and testing models. Further studies on the effects of size structure and studies on behavior should be informative.

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49 Figure 3-1. Daily per capita predation by T. rutilus as a function of predator:prey size ratio (determined by average instar). Solid line is the model exp(K – (ratio*L), where K and L were parameters estimated from the data.

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50 Figure 3-2. Daily per capita predation by T. rutilus as a function of time (days). Solid line is the model (1/(H+exp(-I*(day-J)))) where H, I, and J are parameters estimated from the data.

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51 Figure 3-3. Daily per capita predation by C. appendiculata as a function of predator: prey size ratio (determined by average instar). Solid line is the model (1/(1+exp(-B*(ratio-A)))) where A and B were parameters estimated from the data.

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52 Figure 3-4. Daily per capita predation by C. appendiculata as a function of time (days). Solid line is the model E/(Day + F), where E and F were parameters estimated from the data.

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53 Figure 3-5. Predicted and actual ( SE) per capita mortality of prey in combined predator treatments. Predicted values were calculated from the single predator treatments using the multiplicative risk model (Soluk and Collins 1988). 14 12 10 8 Day 6 Predicted Predation 95 % Confidence Actual Predation 4 2 0 0.6 0.5 0.4 0.3 0.2 0.1 0 Per Capita Mortality

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54 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 0 5 10 152025303540455055DaysProportion of prey surviving C. appendiculata (n = 800) T. rutilus (n = 800) C. appendiculata and T. rutilus (n = 700) Figure 3-6. Survival curves for survival of A. albopictus and O. triseriatus (combined). Bars are standard errors of the life table estimate from PROC LIFETEST (SAS 1989). Curves end when all prey have been consumed, died or emerged as adults.

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0.8 0.7 A. albopictus O. triseriatus 0.6 a 0.5 Survivorship 0.4 b 0.3 B 55 0.2 0.1 A c c C 0 C -0.1 Control Figure 3-7. Mean survivorship (propolarvae surviving to adulthood) of A. albopictus and O. triseriatus ( SE). The absence of a common lower case or upper case letter beside a mean indicates significant differences between predator treatments resulting from pairwise comparisons (p < 0.01) for A. albopictus and O. triseriatus respectively. rtion of the original number of C. appendiculata T. rutilus Both predators

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40 35 A. albopictus O. triseriatus 30 A 56 Figure 3-8. Mean time to adulthood for A. albopictus and O. triseriatus ( SE). Lower case and upper case letters indicate significant differences among predator treatments resulting from pairwise comparisons (p < 0.01) for A. albopictus and O. triseriatus respectively. 0 5 10 1Develo p mene t Tim ( Da y s ) 25 A A 20 A 5 a b c c Control Both Predators C. appendiculata T. rutilus

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0.4 A. albopictus 0.35 O. triseriatus 0.3 B AB 0.25 a a Mass (mg) 0.2 a AB a A 0.15 57 0.1 0.05 0 Control C. appendiculata T. rutilus Both predators Figure 3-9. Mean dry mass of male A. albopictus and O. triseriatus adults ( SE). Lower case and upper case letters indicate significant differences among predator treatments resulting from pairwise comparisons (p < 0.01) for A. albopictus and O. triseriatus respectively.

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0.7 A. albopictus 0.6 O. triseriatus B 0.5 AB 0.4 Mass (mg) b 0.3 b ab 58 A a 0.2 A 0.1 0 Control C. appendiculata Both predators T. rutilus Figure 3-10. Mean dry mass of female A. albopictus and O. triseriatus adults ( SE). Lower case and upper case letters indicate significant differences among predator treatments resulting from pairwise comparisons (p < 0.01) for A. albopictus and O. triseriatus respectively.

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1.2 a a 1 A 0.8 A. albopictus O. triseriatus 0.6 Lambda AB 0.4 AB 59 0.2 b b B 0 -0.2 Control Both Predators T. rutilus C. appendiculata Figure 3-11. Mean estimates of population performance (', an analog of the finite rate of increase for the cohort) for A. albopictus and O. triseriatus adults ( SE). Lower case and upper case letters indicate significant differences among predator treatments resulting from pairwise comparisons (p < 0.01) for A. albopictus and O. triseriatus respectively. The line at ' = 1 is where the population is being replaced, neither increasing nor decreasing.

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Table 3-1. Parameter estimates for the combined predation model. Numbers in parentheses are 95 % confidence intervals. Underlined parameters are the best estimates from the single predator treatments. NLL values indicate the fit of the model to the data. Model Parameter Estimates 60 I E F H J D NLL D is estimated 0.305 0.1926 -0.25585 0.421 7.926 4.661 91.22 (2.68, 6.56) E,F,H,I, and J estimated 1.349 0.242 -0.272 0.262 6.982 1 58.81 (0.18, 0.32) (-0.48, 0.01) (0.22, 0.32) (0.78, 2.33) (6.47, 7.64) All parameters estimated 1.357 0.242 -0.272 0.265 6.965 1.585 58.78 (0.18, 0.33) (-0.48, 0.03) (0.14, 0.47) (0.75, 5.34) (6.20, 7.73) (-71.55, 22.43) 0.305 E and F estimated 0.053 -0.825 0.421 7.926 1 79.48 (0.03, 0.09) (-0.92, -0.07) H,I,J estimated 0.956 0.1926 -0.25585 0.278 7.062 1 69.38 (0.23, 0.35) (0.52, 1.73) (6.44, 8.18)

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61 Top-down and bottom-up effects may be even more apparent in relatively compact habitats such as treeholes and artificial containers where the primary resource base Previous work has focused on determining whether aquatic communities are primarily controlled by top-down (e.g. Brooks and Dodson 1965, Kerfoot and Sih 1987, Gilliam 1989, Diehl 1995, Blaustein 1996, Batzer 2000, Schmitz and Suttle 2001, Williams et al. 2003) or bottom-up (Crowder et al. 1988, Lamberti 1989, Hansson 1992, Kendall et al. 1995, Paradise 1999) regulation. Top-down regulation occurs when the top predator controls the abundance of the trophic levels below it (Paine 1966, Paine 1974). Hairston et al. (1960) proposed that predators regulate herbivores, releasing plants from the effects of herbivory and thus allowing them to reach densities where they are primarily resource-limited. Under this framework, herbivores and detritivores are predator-limited while plants and predators are resource-limited. Bottom-up regulation is the opposite of top-down regulation, and thus occurs when species abundance and trophic levels are controlled by resource levels (Oksanen et al. 1981). Due to bottom-up effects, abundance and/or diversity of predators and/or herbivores have been found to increase with increasing nutrient levels (Hall et al. 1970, Hurd 1971, Crowder et al. 1988, Neill and Peacock 1980). Thus, it is important to understand both top down and bottom up effects on the coexistence of competing prey species (Leibold 1996). CHAPTER 4 EFFECTS OF C. APPENDICULATA DENSITY AND RESOURCE QUANTITY ON PREY PERFORMANCE AND COEXISTENCE. Introduction

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62 originates from allochthonous sources of leaf litter (Macia and Bradshaw 2000, Kitching 2001) and a more minor contribution from stemflow (Carpenter 1982). A number of studies from both natural and artificial container systems have reported the effects of predators (Bradshaw and Holzapfel 1983, Lounibos 1983, 1985, Chambers 1985, Fincke and Yanoviak 1997, Lounibos et al. 2001) or resource quantity (Fish and Carpenter 1982, Carpenter 1983, Leonardo and Juliano 1995, Walker et al. 1997), while relatively few have studied the combined effects of predation and resource levels on prey populations (except see Yanoviak 2001, Kneitel and Miller 2002). When treeholes are first formed, or when containers are first colonized in the field, available resources may be scarce. However, leaf density may change seasonally and leaves may decay, creating fluctuations in resource levels. Furthermore, since the prey larvae are aquatic, they are not able to leave their relatively small microcosms in search of alternative resources until they become adults. Top-down effects in container systems are somewhat variable, especially when predator populations are patchy (Lounibos et al. 1997). Temperate container systems typically contain only a few predator species (Snow 1949, Kitching 2000). The most well studied of these predators, T. rutilus, exerts strong effects on prey assemblages (Bradshaw and Holzapfel 1983, Lounibos et al. 1993). However, T. rutilus is also highly cannibalistic, which may result in only a single predator per container (Lounibos et al. 1996, Campos and Lounibos 2000b). Large numbers of another predator species, C. appendiculata, are commonly found together in a single container (Lounibos 1983), which may result in intraspecific interactions among this species. Unlike T. rutilus, cannibalism does not appear to occur commonly among fourth instar C. appendiculata

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63 (personal observation). Since they are ambush predators, C. appendiculata may not encounter conspecifics often when prey are readily available, and thus this species may only have additive effects on prey mortality. Thus, increases in C. appendiculata density may have significant impacts on prey populations with limited intraspecific interference. Bottom-up effects are assumed to be quite strong in container habitats (Kitching 2001). Resource levels in containers are variable in both space and time. Each geographic region will have its own distinct tree species or subspecies with leaves that may decay at different rates (Dieng et al. 2002) and thus influence the nutrient level available to mosquito larvae. Quantity of leaf litter is also highly variable (Leonard and Juliano 1995, Wynn and Paradise 2001) and has been found to range from 0 to 3.9 g in natural treeholes (Walker and Merritt 1988). Resource levels are also greatly depleted as time goes on, especially in the presence of detritivores such as mosquito larvae (Carpenter 1982). Resource levels in container systems are important in determining species richness, especially early on in the colonization process (Yanoviak 2001); thus experiments manipulating resource quantity are important to determine community structure in these systems. Previous research has found that C. appendiculata may reduce competition among larvae of A. albopictus and O. triseriatus (Chapter 3), most likely through prey depletion, facilitating coexistence of these prey species. Since A. albopictus is the superior resource competitor and the preferred prey of C. appendiculata, a hypothesis can be made based on varying levels of resource and predator densities. I hypothesize that predator density and resource density will interact so that O. triseriatus will be more likely to coexist with A. albopictus at high resource levels and high predator densities. This hypothesis is

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64 based upon models that predict good resource competitors that are vulnerable to predation will dominate at low resource levels, but poor resource competitors that are resistant to predation will replace the other species as resource levels increase (Holt et al. 1994, Leibold 1996, Chase 1999, Chase et al. 2002). The following experiment was conducted to test this hypothesis. Materials and Methods Predator and prey species were obtained in the same way as previously (See Chapter 3). Live Oak (Quercus virginiana) leaves were collected from the ground during the early spring of 2003 in Gainesville, FL. The leaves were washed and dried at 65C for 48 hours. The dried leaves were weighed in portions of 0.5 g and were chopped into pieces ~1cm X 1cm. The leaves were then added to 400 ml sieved tire water (180 m mesh) and soaked for 3 days before the start of the experiment. Two days before the start of the experiment, C. appendiculata larvae that had molted to 4th instars in the past 24 hours were given aquatic nematodes ad libitum. To standardize hunger, C. appendiculata were then starved 24 hours before the start of the experiment. Treatments consisted of 0, 1, 2, or 4 fourth instar C. appendiculata and 0.5, 1.0, or 2.0 g (herein referred to as low, intermediate and high) of oak leaves. Each treatment received 50 first instar A. albopictus and 50 first instar O. triseriatus. Prey larvae less than 24 hours old were added to each container and allowed to acclimate for 10 minutes before adding predators. The experiment was run until all prey larvae had died, been consumed or had emerged. Emerged adults were frozen until all could be thawed then dried together for 48 hours at 65C and weighed individually to the nearest 0.001 mg on a microbalance.

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65 Data Analysis Survivorship, median development time, male and female dry mass, and were each analyzed using a two-way analysis of variance with resource and predator levels as categorical variables in SAS (SAS Institute 1989) for each prey species. Comparisons among treatments were run on least-square means, where one factor is fixed while the others vary (e.g. compare predator treatments with fixed resources). When significant main effects were found, all pairwise comparisons were made using the Tukey-Kramer method (SAS Institute 1989). When significant interactions were found, pairwise comparisons were made between resources for a specific predator treatment (e.g. No Predator: low resource vs. high resource), and between treatments for a specific resource (e.g. low resource: no predator vs. one predator), using Bonferroni correction for experimentwise error. Assumptions of normality and homogeneity of variance were assessed visually and through normality tests. Data that did not meet these assumptions were transformed. Survivorship (proportion prey surviving) was arcsiny transformed. Median development time for O. triseriatus was log (y+1) transformed. Median development time for C. appendiculata was y transformed. Female dry mass of O. triseriatus and A. albopictus median development time were reciprocal transformed. Lambda values did not meet assumptions required for ANOVA and no typical transformation could remedy this. Lambda values were analyzed by a two-way ANOVA and a randomization ANOVA (Manly 1991) and results were compared. Randomization ANOVAs are more robust than other nonparametric analyses and will allow for testing interactions (Crowley 1992). Randomization ANOVAs were run in RT (Manly 1991, Manly 1997) with 1000

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66 randomizations. Since the parametric and randomization ANOVAs yielded the same conclusions, analyses from the parametric ANOVAs will be reported. Lambda values were calculated as previously described (Chapter 3). Lambda values for each treatment were compared against a value of 1 via a t-test to determine if the population would increase, decrease or stay the same. Results Prey Performance Survivorship There was a significant effect of predator (p < 0.0001) and resource levels (p < 0.0001), and their interaction (p < 0.0001) on A. albopictus survivorship (Table 4-1). At low and intermediate resource levels, survivorship was not significantly different among any of the predator treatments. The significant interaction resulted from divergences of trends at high resource levels where survivorship in treatments without predators was significantly higher than all other treatments and survivorship in treatments with four predators was significantly lower than all other treatments (Fig. 4-1, Table 4-2). There was a significant effect of predator (p < 0.0001) and resource levels (p < 0.0001) and their interaction (p < 0.001) on O. triseriatus survivorship (Table 4-3). Survivorship did not differ between predator treatments at low or intermediate resource levels. However, at high resource levels, O. triseriatus survivorship was significantly higher in treatments with one or two predators than in treatments with four or no predators (Fig. 4-2, Table 4-4). The proportion of A. albopictus surviving was determined by dividing the number of A. albopictus surviving by the total number of prey surviving. There was a significant effect of predator (p < 0.0001) and resource levels (p <0.0001) and their interaction ( p <

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67 0.001) on the proportion of A. albopictus surviving (Table 4-5). At low resource levels, a lower proportion of A. albopictus survived with four predators than in any other treatment. At intermediate resource levels, the mean survivorship of A. albopictus increased with four predators, while decreasing in other treatments so that there was not a significant difference among four and one predator treatments. At high resource levels, the mean survivorship of A. albopictus did not differ among predator treatments, but was significantly less in these treatments than in those without predators. Treatments without predators and those with four predators did not differ among resource levels. With one predator, the mean proportion surviving was significantly less at high resource levels than at low resource levels. With two predators, the mean proportion of A. albopictus surviving at high resource levels was significantly lower than at other resource levels (Fig. 4-3, Table 4-6). There was a significant effect of predator (p < 0.0001) and resource level (p <0.0001) and their interaction (p < 0.001) on the total prey survivorship (Table 4-5). At low and intermediate resource levels there was no significant difference in mean survivorship among predator treatments. However, at high resource levels, mean survivorship was significantly lower in treatments with four predators (Fig. 4-4, Table 4-7). Development time There were significant effects of resource (p < 0.0001) and predator levels (p < 0.03) but not their interaction on median development time of A. albopictus (Table 4-1). In the absence of predators, development time of A. albopictus was significantly greater in the low resource treatment than in the intermediate and high resource treatment (Fig. 4-5, Table 4-8).

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68 There was a significant effect of resource (p < 0.0001), but not of predator level or their interaction on median development time of O. triseriatus adults (Table 4-3). Since some containers did not produce any O. triseriatus adults, some comparisons could not be estimated. O. triseriatus took less time to reach adulthood in treatments with high levels of resource as compared with intermediate resource levels (Fig. 4-6, Table 4-9). Adult mass There were significant effects of predator (p < 0.004) and resource levels (p < 0.001), but not their interaction on dry mass of A. albopictus females (Table 4-1). Females emerging from treatments with four predators were significantly larger than those emerging from treatments without predators. Females emerging from treatments with high resource levels were significantly larger than those from treatments with low resource levels (Fig. 4-8, Table 4-10). There were significant effects of predator (p < 0.0001) and resource levels (p < 0.0001), but not their interaction, on dry mass of A. albopictus males. Males emerging from treatments with predators were significantly larger than those from treatments without predators. Males emerged significantly larger with increasing resource levels (Fig. 4-7, Table 4-11). Adult masses of female O. triseriatus were not significantly affected by predator or resource levels. (Fig. 4-10, Table 4-3, 4-12?). There were significant effects of predator (p < 0.04), resource (p < 0.03), and their interaction (p < 0.05) on mass of adult O. triseriatus males (Table 4-3). At high resource levels, males in treatments with four predators emerged significantly larger than those in treatments without predators (Fig. 4-10, Table 4-13).

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69 Lambda There were significant effects of resource (p < 0.001) but not of predator levels on lambda values for A. albopictus (Table 4-1). Lambda values were higher for treatments with high resources than low resources (Fig. 4-11, Table 4-14). There were significant effects of predator (p < 0.001) and resource (p < 0.0001), on lambda values for O. triseriatus (Table 4-3). Lambda values for treatments with one or four predators were greater than those for treatments without predators. LS mean lambda values for treatments with low resource levels were significantly lower than means from intermediate and high resource level (Fig. 4-12, Table 4-15). Predator Performance Survivorship of C. appendiculata was not significantly different among any of the treatments (Table 4-16). There were significant effects of predator (F = 5.24, p < 0.02), but not resource or interaction on developmental time (Table 4-16). C. appendiculata took longer to develop in treatments with four predators than in those with one predator (Fig. 4-13). Dry mass of C. appendiculata adults was significantly affected by resource levels (F = 9.64, p < 0.001), but not by predator levels or the interaction of these factors (Table 4-16). Overall, predators with high resources emerged larger than those in low and intermediate resource levels (Fig. 4-14). Discussion In this study, top-down and bottom-up effects were important in determining prey fitness and coexistence. In general, increased resources resulted in greater survivorship with larger and faster developing prey larvae. Predation was very important in mediating coexistence between prey species. Bottom-up and top-down effects have been shown to be important in other aquatic systems, interacting in some cases (Pagano et al. 2003),

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70 while acting independently in others (Forrester et al. 1999, Yanoviak 2001, Nystrom et al. 2003). Regardless of whether or not these effects interact, predation and resource quantity are both important processes involved in regulating community structure, and the balance and importance of each may change in different environments (Leibold 1989). The results of this study are similar to models that predict good resource competitors that are vulnerable to predation will dominate at low resource levels, but poor resource competitors that are resistant to predation will replace the other species as resource levels increase (Holt et al. 1994, Leibold 1996). Although O. triseriatus was not replaced by A. albopictus, the proportion of emerged adults that were A. albopictus was less than half at high resources and levels of predation (Fig. 4-3). Proulx and Mazumbder (1998) hypothesized that in herbivore-grazer systems, at high levels of productivity, predators would allow the persistence of the less vulnerable species, but not the extinction of the more vulnerable species due to its higher growth rate. This hypothesis is supported in this system where the more vulnerable species, A. albopictus, has a higher growth rate than the less vulnerable species, O. triseriatus. In this experiment, the absence of a predator led to a very low survivorship of O. triseriatus larvae (Fig 4-2). Greater numbers of A. albopictus than O. triseriatus survived at low and intermediate levels of predators with low and intermediate levels of resource. However, survivorship was similar between the two prey species at high resource levels with low and intermediate levels of predation, indicating the potential for coexistence (Figs. 4-1, 4-2). Since there was no point in the current experiment where A. albopictus failed to persist, and coexistence of prey species was facilitated by high resource levels, higher resource

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71 and predator densities may be needed to determine the full spectrum of levels of displacement between these prey species. In treeholes where mosquito occupants are not limited by resources (Bradshaw and Holzapfel 1983), extinction of A. albopictus may not occur. Resource depletion, although not measured directly, seemed fairly evident since some O. triseriatus larvae died before pupation even at the highest resource level. Larval death may have also resulted from build up of toxins from excretory products in the form of ammonium ions (Carpenter 1982) or tannins (David et al. 2000) since containers were not flushed. However, Walker et al. (1991) found a mean of 0.013 mg leaf litter per mosquito larva in treeholes in Indiana which is consistent with intermediate resource levels of 0.01 mg per larva used in the current experiment. Thus, although resource levels may not have been ideal for meeting model assumptions, they were consistent with natural resource levels. Additionally leaves from Live Oak leaves may have toxic compounds that are detrimental to larval survivorship (Lounibos personal communication). As hypothesized by other researchers (Kitching 2001), bottom-up effects were strong in this experiment. (Fig. 4-4). As seen in previous experiments (Leonard and Juliano 1995, Daugherty et al. 2000, Teng and Apperson 2000), increased resource levels generally resulted in increased growth rates, survivorship and mass, and thus increased values for lambda for both prey species. In the absence of predators, O. triseriatus may be driven to extinction, thus predation is very important for the survival of this prey species. Numerous studies have shown that A. albopictus has strong negative effects on O. triseriatus (Ho et al. 1989, Livdahl 1991, Novak et al. 1993, Barrera 1996, Teng and Apperson 2000); however, A. albopictus and O. triseriatus appear to coexist in Florida

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72 treeholes (Lounibos et al. 2001). Combined prey survivorship was not affected by increasing resources when predation was high indicating that high levels of predation will depress prey populations regardless of resource levels. Survivorship of O. triseriatus was similar in both high predator densities and in the absence of predators (Fig. 4-2), indicating that compensatory mortality (Washburn 1995) was occurring where predation and competition were acting similarly on survivorship. The direct effects of predation typically result in decreased development time of prey whether through release from competition (Fauth 1990) or by preferential consumption slow developing prey (Travis et al. 1985, Wilbur 1987, except Tejedo 1993). However, predators may also indirectly affect prey foraging rates and in turn may slow prey development (Van Buskrirk and Yurewicz 1998, Eklov 2000). In the current experiment C. appendiculata did not significantly affect development time for either prey species, although at low resource levels predation decreased the development time of A. albopictus. Likewise, a decrease in development time for O. triseriatus with increased predator densities was noted (Fig. 4-6). At low predator and resource densities, it is possible that predation did not release prey from competitive effects. However, at high predator densities, prey survival was greatly reduced to 5-10 % of initial prey densities, and competition should have been greatly reduced. One possibility is that the predator may have been competing with prey for resources since C. appendiculata will consume microorganisms in early instars (Grabham 1906). When alternative prey are not available, C. appendiculata have been seen browsing on leaves (personal observation). Although this experiment provides information on only a single generation of prey, predictions relating to the abundance of prey in subsequent generations may be useful for

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73 larger scale and long term research on this system. Lambda provides a composite index for predicting whether a population will remain the same, decrease, or increase in the next generation based on survivorship, development time, mass and female fecundity. Even though C. appendiculata allowed a greater proportion of O. triseriatus to survive to adulthood, A. albopictus may have benefited from C. appendiculata as much as or more than O. triseriatus. At low and intermediate levels of predation, lambda values predict that A. albopictus will increase in abundance in the next generation (Fig. 4-11). However, even in the presence of predators, lambda values did not predict that O. triseriatus would increase in abundance (Fig. 4-12). Thus, increases in the abundance A. albopictus and only replacement of O. triseriatus may change the prey ratio for the next generation, allowing increasing numbers of A. albopictus to survive. As a result, O. triseriatus numbers may decrease, even in the presence of C. appendiculata. However, at high levels of predation, lambda values for both prey species are very similar, indicating that both prey species can coexist for a longer period of time at this predator density. At high levels of predation, survivorship of A. albopictus was low , however lambda values predict an increase in the population, similar to the control. Since lambda values depend on survivorship, compensatory mechanisms such as an increase in mass or decrease in development time may have been used by this species. Since C. appendiculata selectively consumes the superior competitor, A. albopictus, at all predator densities, and appears to promote coexistence among prey, it may act as a keystone predator in this system (Paine 1966). However, in the presence of the top predator, T. rutilus, C. appendiculata may be consumed and may not play the primary role of keystone predator (Lounibos 1985, Chapter 3). When prey are not being replaced in the container, C.

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74 appendiculata has been found to influence species composition, while T. rutilus ultimately influences prey numbers (Chapter 3). Field studies have found little to no correlation between co-occurences of the two predator species (Bradshaw and Holzapfel 1983, Lounibos 1983). Additionally, container systems are patchy and there may be cases where the two predators do not co-occur. Interference among predators is commonplace in the literature (Morin 1995, Ferguson and Stiling 1996, Crowder et al. 1997, Lang 2003) although additive (Beachy 1994, Fauth and Resitarits 1991, Gonzalez and Tessier 1997, Dinter 2002) and synergistic (Hixon and Carr 1997, Losey and Denno 1998) effects have also been found. An increase from one to two C. appendiculata larvae resulted in similar prey survivorship for both prey species, indicating that the predators interfere with each other. Another indication of interference among predators was that development time of C. appendiculata was also influenced by the presence of conspecifics. High predator densities resulted in longer time to adulthood for C. appendiculata. This may be a result of intraspecific competition among the predator species. In other words, although the same number of prey in consumable instars was maintained among predator treatments, the predators had to share the number of consumable prey as predator density increased. Thus, increased densities of C. appendiculata larvae may result in less than expected prey consumption and negative interactions. Menge (1992) hypothesized that if top-down and bottom-up effects are linked, one should expect stronger predation with increased nutrients because an increase in nutrients would support more prey and thus more predators. At high nutrient levels, C. appendiculata adults emerged larger than those at lower nutrient levels (Fig. 4-14). Since

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75 fecundity is typically linked to increase in mass or wing length (e.g. Armbruster and Hutchinson 2002), this would suggest that higher nutrient levels will lead to more predators, and thus more intense predation. Two possible mechanisms may explain larger C. appendiculata adults. Bottom-up cascades have been found in other aquatic systems (Hyatt and Stockner 1985, Leibold 1989, reviewed in:Hunter and Price 1992) and thus may have resulted in the increased mass of C. appendiculata adults. The increased resources available to the prey could have increased the amount of biomass available to C. appendiculata and allowed the predators to emerge at a larger size. However if prey growth rate was increased at high resource levels, this may have been detrimental to C. appendiculata since they can only consume smaller prey items (Lounibos 1983) and faster developing prey are more likely to escape predation. Alternatively, C. appendiculata could have been directly consuming microfauna and microbes produced as a result of increasing quantity of leaf litter. As a result of this, C. appendiculata may act as an intraguild predator and compete with its prey for resources. Further studies should investigate the natural diet of this predator species and its role in intraguild predation among mosquito larvae. This study was run under rather warm temperature conditions, approximating those found in Florida during the summer (Chapter 3). A. albopictus has been shown to increase its competitive advantage over O. triseriatus with increasing temperature, and Teng and Apperson (2000) have even suggested that at low temperatures, O. triseriatus will have negative effects on A. albopictus. Furthermore, Livdahl and Willey (1991) predicted that A. albopictus may exclude O. triseriatus in tire fluid but not in treehole fluid, suggesting that resources in tire fluid can be more effectively utilized by A.

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76 albopictus than by O. triseriatus, while the two species may differentially utilize resources in treehole fluid. Tire water may also be a more limiting resource than treehole fluid (Livdahl and Willey 1991), which may further disadvantage the inferior competitor, O. triseriatus. This hypothesis was supported in the current experiment where O. triseriatus was almost completely excluded by A. albopictus in the absence of predators. Nannini and Juliano (1998) also provided indirect support for the above hypothesis by finding higher survivorship of O. triseriatus in treeholes than in tires. Since the current study utilized tire fluids, the effects of predator and resource densities on the proportion of A. albopictus survival may be further exaggerated, favoring survival of A. albopictus over O. triseriatus. Top-down and bottom-up effects were important for both the predator and prey species in this study. The complexity of interactions observed may not be detectable under manipulations of only top-down or bottom-up effects. Thus, experiments that simultaneously manipulate both of these factors may be useful in determining community structure in container systems. Further studies should also focus on long term, large scale studies in both temperate and tropical container systems since tropical systems may have more complex food webs composed of both vertebrate and invertebrate predators (Fincke 1999).

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77 Figure 4-1. Mean survivorship (propo rtion of the original number of larvae surviving to adulthood) of A. albopictus ( SE) at three levels of resources and four levels of predation. 0 0.1 0 0Survivorship .2 .3 0.4 0.5 0.6 0.7 0.8 0.9 Resource No Predator 1 C. appendiculata 2 C. appendiculata 4 C. appendiculata Low Intermediate High

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0.6 No Predator 1 C. appendiculata 2 C. appendiculata 0.5 4 C. appendiculata 0.4 Survivorship 0.3 0.2 78 0.1 0 High Low Intermediate Resource Figure 4-2. Mean survivorship (proportion of the original number of larvae surviving to adulthood) of O. triseriatus ( SE) at three levels of resources and four levels of predation.

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1.2 79 Figure 4-3. Mean proportion of A. albopictus surviving (number of A. albopictus surviving divided by the number of A. albopictus + O. triseriatus surviving) ( SE) at three levels of resources and four levels of predation. 0 0.2 0.4 0.6 0.8 No. A. albopictus / (No. A. albopictus + O. triseriatus) No Predator 1 C. appendiculata 2 C. appendiculata 1 4 C. appendiculata Low Intermediate High Resource

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0.5 No Predator 0.45 1 C. appendiculata 2 C. appendiculata 0.4 4 C. appendiculata 0.35 0Survivorship 0.3 0.25 0.2 80 .15 0.1 0.05 0 Low High Resource Intermediate Figure 4-4. Mean combined survivorship of both prey species (Number of A. albopictus + O. triseriatus surviving divided by total prey originally present at start of the experiment) ( SE) at three levels of resources and four levels of predation.

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40 35 No Predator 1 C. appendiculata 2 C. appendiculata 4 C. appendiculata 11230Median Development time (days) 25 0 5 81 0 5 0 Low Intermediate High Resource Figure 4-5. Median time to adulthood of A. albopictus ( SE) at three levels of resources and four levels of predation.

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60 No Predator 1 C. appendiculata 50 2 C. appendiculata 4 C. appendiculata 40 30 82 20 10 Median Development Time (days) 0 Low Intermediate High Resource Figure 4-6. Median time to adulthood of O. triseriatus ( SE) at three levels of resources and four levels of predation.

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0.6 0.5 0.4 0Mass (mg) 0.3 83 .2 No Predator 0.1 1 C. appendiculata 2 C. appendiculata 4 C. appendiculata 0 Low Intermediate High Resource Figure 4-7. Mean adult mass of male A. albopictus ( SE) at three levels of resources and four levels of predation.

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0.7 0.6 0.5 Mass (mg) 0.4 0.3 84 0.2 No Predator 0.1 1 C. appendiculata 2 C. appendiculata 4 C. appendiculata 0 Low Intermediate High Resource Figure 4-8. Mean adult mass of female A. albopictus ( SE) at three levels of resources and four levels of predation

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0.6 No Predator 1 C. appendiculata 2 C. appendiculata 0.5 4 C. appendiculata 0.4 Mass (mg) 0.3 85 0.2 0.1 0 Low Intermediate High Resource Figure 4-9. Mean adult mass of male O. triseriatus ( SE) at three levels of resources and four levels of predation.

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1.2 NoPredator 1 C. appendiculata 2 C. appendiculata 1 4 C. appendiculata 0.8 Mass (mg) 0.6 0.4 86 0.2 0 Low Intermediate High Resource Figure 4-10. Mean adult mass of female O. triseriatus ( SE) at three levels of resources and four levels of predation.

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1.2 1 0.8 0.6 87 0.4 No Predator 0.2 1 C. appendiculata 2 C. appendiculata 4 C. appendiculata 0 Low High Intermediate Resource Figure 4-11. Mean estimates of population performance (', an analog of the finite rate of increase for the cohort) ( SE) for A. albopictus at three levels of resources and four levels of predation. The line at ' = 1 is where the population is being replaced, neither increasing nor decreasing.

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1.2 1 0.8 0.6 ' 0.4 88 No Predator 0.2 1 C. appendiculata 2 C. appendiculata 4 C. appendiculata 0 Low Intermediate High Resource Figure 4-12. Mean estimates of population performance (', the composite index of fitness for the cohort) ( SE) for O. triseriatus at three levels of resources and four levels of predation. The line at ' = 1 is where the population is being replaced, neither increasing nor decreasing.

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25 20 Development time(Days) 15 10 89 5 1 C. appendiculata 2 C. appendiculata 4 C. appendiculata 0 Low Intermediate High Resource Figure 4-13. Mean development time to adulthood of C. appendiculata ( SE) at three levels of resources and four levels of predation.

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0.18 0.16 0.14 0.12 Mass (mg) 0.1 0.08 90 0.06 0.04 1 C. appendiculata 0.02 2 C. appendiculata 4 C. appendiculata 0 Low Intermediate High Resource Figure 4-14. Mean adult mass of C. appendiculata ( SE) at three levels of resources and three levels of predation.

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Table 4-1. Two way ANOVA for , survivorship to adulthood, development time, and adult mass for A. albopictus. Mass , estimated finite rate of increase Survivorship a DevelopmentTime b Males Females Source df F P F P F P F P F P Predator 3 1.22 0.3115 29.98 0.0001 3.26 0.0299 12.76 0.0001 5.50 0.0033 Resource 2 7.99 0.0010 58.82 0.0001 60.34 0.0001 33.78 0.0001 11.58 0.0001 Predator*Resource 6 1.04 0.4087 17.73 0.0001 1.44 0.2206 1.02 0.4283 0.82 0.5625 Error df 48 c a arcsiny transformed, b – reciprocal transformed, c – For development time and mass error df was 46 91

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Table 4-2. Results of Tukey tests of effects of Predator and Resource on A. albopictus survivorship.Mean values with a common underline are not significantly different for main effects (p < 0.05). 92

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Table 4-3. Two way ANOVA for , survivorship to adulthood, development time, and adult mass for O. triseriatus Mass , estimated finite rate of increase Survivorship a DevelopmentTime b Males Females Source df F P F P F P F P F P Predator 3 7.75 0.0003 14.02 0.0001 2.80 0.0549 3.32 0.0346 1.42 0.2597 Resource 2 14.69 0.0001 58.88 0.0001 16.64 0.0001 4.11 0.0276 2.73 0.0840 Predator*Resource 6 1.49 0.2027 5.72 0.0002 0.30 0.9111 2.89 0.0411 1.39 0.2621 Error df 48 d a arcsiny, b – log(y+1), c – 1/y, d For development time and mass error df was 33 93

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Table 4-4. Results of Tukey tests of effects of Predator and Resource on O. triseriatus survivorship. Mean values with a common underline are not significantly different for main effects (p < 0.05). 94

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Table 4-5. Two way ANOVA for proportion of A. albopictus : O. triseriatus surviving and combined prey survivorship. ProportionA. albopictus : O. triseriatus a Combined Survivorship a Source df F P F P Predator 3 11.96 0.0001 11.52 0.0001 Resource 2 90.65 0.0001 104.11 0.0001 Predator*Resource 6 6.84 0.1628 5.74 0.0001 Error df 48 a arcsiny 95

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Table 4-6. Results of Tukey tests of effects of Predator and Resource on the proportion of A. albopictus surviving. Mean values with a common underline are not significantly different for main effects (p < 0.05). 96

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Table 4-7. Results of Tukey tests of effects of Predator and Resource on total prey survivorship. Mean values with a common underline are not significantly different for main effects (p < 0.05). 97

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Table 4-8. Results of Tukey tests of effects of Predator and Resource on median time to adulthood for A. albopictus. Mean values with a common underline are not significantly different for main effects (p < 0.05). 98

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Table 4-9. Results of Tukey tests of effects of Predator and Resource on median time to adulthood for O. triseriatus. Mean values with a common underline are not significantly different for main effects (p < 0.05). 1 – Some treatments did not have any prey surviving to adults. 99

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Table 4-10. Results of Tukey tests of effects of Predator and Resource on female mass of A. albopictus adults. Mean values with a common underline are not significantly different for main effects (p < 0.05). 100

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Table 4-11. Results of Tukey tests of effects of Predator and Resource on male mass of A. albopictus adults. Mean values with a common underline are not significantly different for main effects (p < 0.05). 101

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Table 4-12. Results of Tukey tests of effects of Predator and Resource on female mass of O. triseriatus. Mean values with a common underline are not significantly different for main effects (p < 0.05). Predator None One Two Four non-est 0.423 0.312 0.474 Resource Low Intermediate High non-est 0.291 0.488 102

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Table 4-13. Results of Tukey tests of effects of Predator and Resource on male mass of O. triseriatus adults .Mean values with a common underline are not significantly different for main effects (p < 0.05). 1 – Some treatments did not have any prey surviving to adults. 103

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Table 4-14. Results of Tukey tests of effects of Predator and Resource on for A. albopictus . Mean values with a common underline are not significantly different for main effects (p < 0.05). 104

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Table 4-15. Results of Tukey tests of effects of Predator and Resource on O. triseriatus . Mean values with a common underline are not significantly different for main effects (p < 0.05). 105

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Table 4-16. Two way ANOVA for , survivorship to adulthood, development time, and adult mass for C. appendiculata 106 Survivorship DevelopmentTime a Mass Source Df F P F P F P Predator 2 0.45 0.6402 5.24 0.0104 1.39 0.2639 Resource 2 0.45 0.6402 0.73 0.4871 9.64 0.0009 Predator*Resource 4 1.48 0.2275 0.80 0.5311 1.53 0.2165 Error df 36 b a y, b For development time and mass error df was 34

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107 CHAPTER 5 EFFECTS OF HABITAT COMPLEXITY AND PREDATION ON PREY COEXISTENCE Introduction In most aquatic systems habitat struct ure typically includes living macrophytes, however in treeholes and artificial containers, leaf litter and other non-living detritus are typically the primary contributo rs to habitat structure, alt hough treeholes themselves may provide structure. Thus habitat complexity changes as senescing leaves fall into containers and treeholes and gradually decay. Increases in habitat complexity have b een known to affect species composition and abundance (Heck and Wetsone 1977, Dean and Connell 1987b), and predation (e.g. O’Flynn and Craig 1982). Increased habitat complexity may result in greater species richness or decreased competition when there is an increase in resource levels and/or niches available (Pianka 1978). When compe tition results in mortality among or between species, increased habitat complexity with an increase in biomass or surface area will increase food and living space and thus lesse n negative effects of competition (Dean and Connell 1987b). A number of studies in a quatic systems have focused on the effects of habitat complexity on actively searching predators such as fish (Heck and Thoman 1981, Savino and Stein 1982, Coull and Wells 1983) as well as aquatic invertebrates (Folsom and Collins 1984, Williams et al.1993). However, only a few have studied the effects of habitat complexity on ambush predators (O’Flynn and Craig 1982, Folsom and Collins

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108 1984, Heck and Crowder 1991, James and Heck 1994). Habitat complexity should have distinctly different effects on the two hunting styles. In systems with visual predators, habitat structure in the form of aquatic macrophytes will typically block the predator from being able to detect the prey visually and thus result in decreased predation rates (Diehl 1988). Ambush predators may detect prey motion, and thus habitat structure may have little effect on this hunting style. In marine systems, with the addition of habitat structure, predation rates were either enhanced or were not affected (Ryer 1988, Dudgeon 1993, James and Heck 1994). The theory behind these results is that increased habitat complexity aids the predator by providing camouflage and reducing the ability of the prey to detect the predator (Coen et al.1981, Howard and Koehn 1985, James and Heck 1994). However, habitat complexity has also been found to reduce predation by an ambush predator of a treehole mosquito community (O'Flynn and Craig 1982). Thus, more studies are needed to determine general patterns and whether habitat complexity influences predation in the Florida treehole community under study. In a previous experiment, resource was manipulated by the addition of leaf litter, which also resulted in the addition of habitat structure (Chapter 4). I hypothesized that habitat complexity would influence predation rates, possibly affecting coexistence among the prey species. Materials and Methods Predator and prey species were obtained and conditioned as described in previous chapters. In order to decrease habitat structure created by resources, Live oak (Quercus virginiana) leaves were dried at 65 C for 48 hours and ground in a blender (Vitamix). This limited large amounts of interstitial space caused by whole leaves, while still

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109 providing prey with natural resources. Three days before the start of the experiment, each container received 400 ml sieved tire water (180 m), 2.0 g of ground leaves and the addition of one of four levels of artificial habitat structure. Habitat structure consisted of artificial, cloth maple leaves (sold commercially for decorating) that had been rinsed three times in hot water and soaked over night to remove any dyes. After cutting in half, each leaf had a surface area of 18.5 cm 2 . Habitat structure consisted of 0, 2, 6, or 10 half leaves . Treatments consisted of 0 or 1 fourth instar C. appendiculata and 0, 2, 6, or 10 half leaves (herein referred to as none, low, intermediate or high) and were replicated five times. Each treatment received 50 first instar A. albopictus and 50 first instar O. triseriatus. Prey larvae less than 24 hours old were added to each container and allowed to acclimate for 10 minutes before adding predators. The experiment was run until all prey larvae had died, been consumed or had emerged. Emerged adults were frozen until all could be thawed then dried together for 48 hours at 65C and weighed individually to the nearest 0.001 mg on a microbalance. Data Analysis Survivorship (proportion surviving) and median development time were analyzed by Two-Way Analysis of variance with predator and habitat as categorical variables in SAS PROC GLM (SAS 1989). Median, instead of mean, development time was used because of skewed distributions caused by a few prey taking an exceptionally long time to become adults. In some cases, data did not meet the assumptions of normality and/or homogeneity of variances and were transformed. Survivorship was arcsin (y)

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110 transformed and median development time was log (y +1) transformed for both prey species. Results There was a significant effect of predator (p < 0.0001), but not of habitat or the interaction of these factors on A. albopictus survivorship (Table 5-1). Survivorship of A. albopictus was greater in the absence of predators. Regression detected a trend, although not significant, toward decreased survivorship of A. albopictus with increasing habitat complexity in the presence of predators (F 1,19 = 3.68, p = 0.07, R 2 = 0.16) (Fig. 5-1). There was a significant effect of predator (p < 0.0001), but not of habitat or the interaction of these factors on O. triseriatus survivorship (Table 5-2). Mean survivorship of O. triseriatus was significantly greater in the presence (LS means 0.259 0.017 ) vs. absence. (0.052 0.018 ), (p < 0.0001).(Fig. 5-2) of predators. There was a significant effect of predator (p < 0.0001), but not of habitat or the interaction of these factors on median development time of A. albopictus (Table 5-1). Development time of A. albopictus was significantly shorter in the presence (LS means 11 0.26 days) vs. absence (13 1.16 days ) of predators (p<0.0001)(Fig. 5-3). Neither manipulated variable nor their interaction had significant effects on the median development time of O. triseriatus (Figure 5-4, Table 5-2). Discussion In this experiment, prey survivorship was primarily influenced by the presence or absence of a predator and not by the degree of habitat complexity. Other studies in aquatic systems have found non-significant effects of habitat complexity on predation rates of ambush predators (Ryer 1988, Dudgeon 1993, James and Heck 1994). Habitat complexity may not be as important for predators that do not chase their prey through

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111 structurally complex habitats. In fact, additional habitat complexity may aid ambush predators by trapping prey in compartments with predators (Flynn and Ritz 1999). Habitat complexity is thought to create niches and increase living space so that more species can coexist (Pianka 1978). This did not appear to be the case in the current experiment since survivorship was similar among different levels of complexity, even in the absence of predators. O. triseriatus survivorship stayed consistently below 10 % with changes in complexity. Although the artificial leaf litter in this study did not appear to contribute to resource levels, the leaves should have provided spaces where the two prey species would not encounter each other as often. Thus, if encounter competition were important, survivorship should increase with habitat complexity. Although habitat complexity did not have a significant effect on either prey species, trends in survivorship were associated with changes in habitat complexity. In treatments with predators, A. albopictus survivorship decreased and O. triseriatus survivorship increased with increasing habitat structure. Since C. appendiculata primarily utilizes a sit-and-wait foraging strategy, prey behavior may have played a large role in the observed trend. The rate of prey movement within a habitat is known to influence the impacts of ambush predators (Swisher et al.1998). Folsom and Collins (1984) found that prey activity decreased in natural habitats with structure as opposed to treatments without any habitat structure. A. albopictus is known to be more active than O. triseriatus, and does not change its behavior in the presence of T. rutilus (Kesavaraju and Juliano 2004). Similar results may occur in the presence of C. appendiculata since it is thought that O. triseriatus may react to chemicals released when prey are consumed, rather than to the actual predator (Kesavaraju personal communication). Hence, C. appendiculata may

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112 have encountered A. albopictus more often than O. triseriatus if similar behavior occurs with other treehole predators. Predator behavior may have also changed with changes in habitat complexity. Flynn and Ritz (1999) found that an ambush predator preferred vegetated habitat to open water when vegetation was provided. C. appendiculata may also change habitats with the addition of habitat complexity since it may be at risk of predation by a top predator, T. rutilus, in natural container systems (Lounibos 1983, 1985, Campos and Lounibos 2000b). Ryer (1988) hypothesized that habitat complexity did not affect predation by an intermediate predator because the small predator may forage less to minimize risk from the top predator, even in the absence of the top predator. C. appendiculata may change its behavior with increasing habitat complexity to maintain similar predation rates. Savino and Stein (1982) found that largemouth bass can switch from searching to ambush predation with increased habitat complexity and thus maintain similar capture rates with increasing complexity. Since predation did not change with habitat complexity, it is likely that C. appendiculata may adapt well to habitat structure. Folsom and Collins (1984) found that when varying habitat complexity, both predator and prey behaviors were dependent upon the specific microhabitat available. Behavioral observations of both predator and prey are needed to determine the mechanisms behind these results. Larger containers may be required to see if any effects of habitat complexity are present. Gotceitas and Colgan (1987, 1989) hypothesized that a threshold of habitat complexity must be reached before the foraging success of predators is reduced. However, in this experiment, the leaf litter went through the entire water column and protruded out of the water at the highest level of complexity, suggesting that any

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113 threshold required should have been reached. Perhaps more natural, larger containers would allow more space between predator and prey, thus decreasing encounter rates to show significant effects of habitat complexity. Although habitat complexity did not appear to be important in this study, further studies are needed to determine whether habitat complexity interacts with predation in container systems. Long term field studies over multiple prey generations would be especially useful if the effects of habitat complexity are weak or are cumulative. Container habitats also harbor potential prey items that are not mosquito larvae such as chironomids, syrphids and other macrofauna (e.g. Campos & Lounibos 2000). Each of these potential prey types may react differently to predation and to increased habitat complexity, especially since some are not as mobile as mosquito larvae.

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114 Figure 5-1. Least square means ( SE) fo r survivorship of A. albopictus. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Habitat Complexity Survivorship Predator absent Predator present None Low Intermediate High

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0.4 Predator absent 0.35 Predator present 0.3 0.25 Survivorship 0.2 115 0.15 0.1 0.05 0 None Low Intermediate High Habitat Complexity Figure 5-2. Least square means ( SE) for survivorship of O. triseriatus.

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116 16 14 12 Development Time (Days) 10 8 6 4 2 Predator Absent Predator Present 0 None Figure 5-3. Least square means ( SE) for median development time of A. albopictus. Habitat Complexity Intermediate Low High

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117 50 45 40 35 Development Time (Days) 30 25 20 15 10 Predator Absent 5 Predator Present 0 None Low Figure 5-4. Least square means ( SE) for median development time of O. triseriatus. Habitat Co mplexity Intermediate High

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118 Table 5-1. Two way ANOVA for survivorship to adulthood and median development time for A. albopictus Survivorship Development Time Source df F P F P Predator 1 35.03 0.0001 27.30 0.0001 Habitat 3 0.40 0.7300 1.48 0.2390 Predator*Habitat 3 1.25 0.2501 00.32 0.8117 Error df 32

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Table 5-2. Two way ANOVA for survivorship to adulthood and median development time for O. triseriatus Survivorship Development Time Source df F P F P Predator 1 73.26 0.0001 0.02 0.8872 Habitat 3 1.80 0.1664 1.33 0.2845 Predator*Habitat 3 0.54 0.6587 0.57 0.6370 Error df 32 119

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120 The effects of both predator species on prey survivorship can be predicted from treatments with each predator alone, indicating additive effects of the predators. However, this was dependent upon the initial sizes of each species added. This may be important in controlling the prey species, since many studies with more than one predator lead to less than additive effects with less prey being consumed. Not only does C. appendiculata have limited effects on prey consumption by T. rutilus, this intermediate predator may also benefit T. rutilus by acting as alternate prey. The above may be When an invasive species colonizes a new habitat, it may not be affected by the native predator guild because the predators may not be adapted for consuming the novel species. The introduction of an invasive species may also reduce biodiversity (Wilcove et al.1998). However, in the current study, the invasive species was preferred over the native species by the two primary aquatic predators in treeholes in Florida. The results suggest that although the invasive species is competitively dominant to the native, the presence of predators, especially C. appendiculata, may allow both prey species to coexist and, perhaps, enhance diversity in this community. In containers without common predators, other mosquito species have been displaced by A. albopictus (Hobbs et al.1991, Mekuria and Hyatt 1995, O'Meara et al.1995). In the current study, the predators may act as buffers to limit the abundance and fitness of the invasive species, A. albopictus. CHAPTER 6 CONCLUSIONS

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121 important when attempting to use predators as biological control agents. C. appendiculata may not affect the efficiency of T. rutilus but may be able to sustain this predator at low levels of alternative prey. C. appendiculata appears to foster coexistence among the invasive and native prey species. In the presence of this predator, the survivorship of A. albopictus decreased while the survivorship of O. triseriatus increased. In some cases, the presence of C. appendiculata was also beneficial to A. albopictus, predicting an increase in population growth at intermediate resource levels in the presence of predators, while the absence of predators predicted a decrease in population growth. C. appendiculata was also beneficial to O. triseriatus, predicting population replacement of this species in the presence of predators, while predicting population decline in the absence of predators. In some cases, the presence of A. albopictus may be beneficial to O. triseriatus by relieving predation pressure on the latter species although this is debatable given the strong negative effects of competition from A. albopictus on O. triseriatus. Thus, direct effects of predation may actually benefit future prey populations by resulting in either replacement or increased abundance, counter to the typical results of predators negatively affecting prey abundance. Intraguild predation seems to be common in treehole communities and may occur on two levels in these communities. The top predator will commonly consume the intermediate predator as well as the prey species. In addition, the intermediate predator will consume the prey species as well as the basal resource, microorganisms. Thus, competition between the intraguild predator and its intraguild prey may be important in predicting the ability of these species to coexist.

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122 Both bottom-up and top-down effects are important in treehole and container communities, indicating that these effects should be studied in conjunction with each other. Predation facilitated the persistence of the native species, but did not result in the extinction of the invasive species, even at high levels of resources and predation intensity. The importance of resources is seen by similar values of lambda for the invasive species at high resource levels. This indicates that in a highly productive environment, predators may not have visible overall effects on A. albopictus populations. Habitat structure is variable in treeholes and is primarily created by the addition of leaf litter. Artificial leaf litter did not alter the effects of predators on prey survivorship. This suggests that C. appendiculata may adapt well to finding prey with increasing levels of habitat structure. Future studies should focus on the effects of this invasive species on members of this community other than mosquito larvae. Field experiments over multiple generations would be useful to determine if the predicted effects discussed will occur with additional environmental and spatial complexity. Quantitative behavioral studies will give insight into the observed predator-prey interactions. Exploring these interactions in a microcosm such as a treehole may be useful in generating general theory for other aquatic habitats.

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APPENDIX A R CODE FOR CHAPTER 3 Single Predator Models: C. appendiculata ***Day Model*** library(mleprof) ***library for maximum likelihood estimates available at http://www.zoo.ufl.edu/bolker/R/windows *** c = read.table("f://cor1.txt",header=TRUE)***Read in data*** attach(c) *** Allows column headers to be used*** ***Likelihood function for two-parameter exponential function *** likfunc = function(p){ E = p[1] F = p[2] prob=E*exp(-F*(Day)) lik=-sum(dbinom(x=consc, size=survc+consc, prob=prob,log=TRUE)) } *** Runs likelihood function with starting values*** m1 = mle(fn=likfunc,start=c(A=0.5,B=0.5)) m1 **Gives parameter estimates and NLL** ***95 % CI on E and F*** intervals(m1,which=1,range=c(-1,1)) intervals(m1,which=2,range=c(-1,1)) ***Per Capita Predation Calculations *** percapc=consc/(survc+consc) plot(Day,percapc,ylab="Per Capita Prey Consumption",xlab="Day",main="Fig C. appendiculata Daily Per Capita Predation") **Plot of data** curve(.2435227*exp(-0.2613315*(x)),add=TRUE) *** Adds curve based on parameter estimates to data*** ***Size Ratio Model *** csize=read.table("f:\\corinstar.txt",header=TRUE) attach(csize) avgp=c((p1+(2*p2)+(3*p3)+(4*p4)+(5*p5))/(p1+p2+p3+p4+p5))**calculates average prey instar** ratioc=avgcor/avgp **calculates Predator:Prey size ratio** percapc=cons/(surv+cons) plot(ratioc,percapc,xlab="Predator:Prey size ratio", ylab="Per Capita Prey Consumption",main="Fig C. appendiculata per capita predation versus predator:prey size ratio") likfunc = function(p){ 123

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124 A = p[1] B = p[2] prob=1/(1+exp(-B*(ratioc-A))) lik=-sum(dbinom(x=cons, size=surv+cons, prob=prob,log=TRUE)) } m1 = mle(fn=likfunc,start=c(A=1,B=1)) m1 aicc=2*103.9399+(2*2) curve(1/(1+exp(-1.799*(x-3.24))), add=TRUE) intervals(m1,which=1,range=c(0,5)) intervals(m1,which=2,range=c(0,3)) T. rutilus ***Day Model*** t=read.table("f://tox1.txt",header=TRUE) attach(t) percapt=const/(survt+const) plot(dayt,percapt,ylab="Per Capita Prey Consumption",xlab="Day",main="Fig T. rutilus Daily Per Capita Predation") likfunt3 = function(p){ H = p[1] I = p[2] J = p[3] prob=H/(1+exp(-I*(dayt-J))) lik=-sum(dbinom(x=const, size=survt+const, prob=prob,log=TRUE)) } m4 = mle(fn=likfunt3,start=c(H=-1,I=-2,J=5)) m4 intervals(m4,which=1,range=c(0,1)) intervals(m4,which=2,range=c(0,1)) intervals(m4,which=3,range=c(0,20)) curve(0.4214894/(1+exp(-0.3047904*(x-7.9255864))),add=TRUE) Testing multiplicative risk model using best models from single predator treatments f1=function(H=-1,I = -2,J=5){ H/(1+exp(-I*(dayt-J))) } f2=function(E=0.2435227,F=-0.2613315){ E/(F+Day) } ct=read.table("f://cortox1.txt",header=TRUE) **reads in combined predator data** attach(ct)

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125 percapct=consct/(survct+consct) **This function is to see how parameter estimates vary when D is 1**** likfun1 = function(p) { H= p[1] I = p[2] J = p[3] E = p[4] F = p[5] pred1exp = f1(H,I,J) pred2exp = f2(E,F) totexp=pred1exp+pred2exp-(pred1exp*pred2exp)**Multiplicative risk model** lik = -sum(dbinom(consct,size=survct+consct,prob=totexp,log=TRUE)) } m1 = mle(fn=likfun1,start=c(H=0.4,I=1.1,J=6,E=0.5,F=-0.1)) m1 intervals(m1,which=1,range=c(0.1,0.4)) intervals(m1,which=2,range=c(0,3)) intervals(m1,which=3,range=c(0,10)) intervals(m1,which=4,range=c(0,.5)) intervals(m1,which=5,range=c(-.5,0.5)) p1 = 0.2620771/(1+exp(-1.3497088*(dayt-6.9819025))) p2 = 0.2421885/(Day 0.2723886) totexp = p1 + p2 -((p1*p2)) plot(day,percapct,ylab="Per Capita Prey Consumption",xlab="Day",main="Fig cortox Daily Per Capita Predation") points(day,totexp,col="red") **plots predicted per capita predation based on parameters estimated above** ***This function is to see how parameters vary when D parameter varies**** likfun2 = function(p) { H= p[1] I = p[2] J = p[3] E = p[4] F = p[5] D = p[6] pred1exp = f1(H,I,J) pred2exp = f2(E,F) totexp=pred1exp+pred2exp-D*(pred1exp*pred2exp) lik = -sum(dbinom(consct,size=survct+consct,prob=totexp,log=TRUE)) lik } m2 = mle(fn=likfun2,start=c(H=0.5,I=1.3,J=7,E=0.5,F=-.28,D=1))

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126 intervals(m2,which=6,range=c(-35,30)) ***This only lets D vary with parameters fixed from single predator treatments**** Hbest = 0.4214894 Ibest = 0.3047904 Jbest = 7.9255864 Ebest = 0.1926 Fbest = -0.25585 likfun3 = function(p) { D = p[1] pred1exp = f1(Hbest,Ibest, Jbest) pred2exp = f2(Ebest,Fbest) totexp=pred1exp+pred2exp-D*(pred1exp*pred2exp) lik = -sum(dbinom(consct,size=survct+consct,prob=totexp,log=TRUE)) lik } m3 = mle(fn=likfun3,start=c(D=1)) intervals(m3,which=1,range=c(0,8)) p1 = 0.4214894/(1+exp(-0.3047904*(dayt-7.9255864))) p2 = .1926/(Day 0.25585) totexp = p1 + p2 -(4.660994*(p1*p2)) plot(day,percapct,ylab="Per Capita Prey Consumption",xlab="Day",main="Fig cortox Daily Per Capita Predation") points(day,totexp,col="red") ***This fixes best parameters from T. rutilus treatments and allows C. appendiculata parameters to vary with D = 1**** Hbest = 0.4214894 Ibest = 0.3047904 Jbest = 7.9255864 likfun4 = function(p) { E = p[1] F = p[2] pred1exp = f1(Hbest,Ibest,Jbest) pred2exp = f2(E,F) totexp=pred1exp+pred2exp-(pred1exp*pred2exp) lik = -sum(dbinom(consct,size=survct+consct,prob=totexp,log=TRUE)) lik } m4 = mle(fn=likfun4,start=c(E=.2,F=-.2)) intervals(m4,which=1,range=c(0,.1)) intervals(m4,which=2,range=c(-.940,1)) p1 = 0.4214894/(1+exp(-0.3047904*(dayt-7.9255864))) p2 = 0.05278505/(Day 0.82545955)

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127 totexp = p1 + p2 -((p1*p2)) plot(day,percapct,ylab="Per Capita Prey Consumption",xlab="Day",main="Fig cortox Daily Per Capita Predation") points(day,totexp,col="red") ***This fixes best parameters from C. appendiculata treatments and allows T. rutilus to vary with D = 1**** Ebest = 0.1926 Fbest = -0.25585 likfun4 = function(p) { H= p[1] I = p[2] J = p[3] pred1exp = f1(H,I,J) pred2exp = f2(Ebest,Fbest) totexp=pred1exp+pred2exp-(pred1exp*pred2exp) lik = -sum(dbinom(consct,size=survct+consct,prob=totexp,log=TRUE)) lik } m5 = mle(fn=likfun4,start=c(H=.3,I=.3, J = 7)) intervals(m5,which=1,range=c(0,.4)) intervals(m5,which=2,range=c(0,3)) intervals(m5,which=3,range=c(0,12)) p1 = 0.2783856/(1+exp(-0.9562559*(dayt-7.0620439))) p2 = 0.1926/(Day -0.25585) totexp = p1 + p2 -((p1*p2)) plot(day,percapct,ylab="Per Capita Prey Consumption",xlab="Day",main="Fig cortox Daily Per Capita Predation") points(day,totexp,col="red") ***D = 1, Fixed parameters from single predator treatments*** Hbest = 0.4214894 Ibest = 0.3047904 Jbest = 7.9255864 Ebest = 0.1926 Fbest = -0.25585 likfun3 = function(p) { D = p[1] pred1exp = f1(Hbest,Ibest, Jbest) pred2exp = f2(Ebest,Fbest)

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128 totexp=pred1exp+pred2exp-(pred1exp*pred2exp) lik = -sum(dbinom(consct,size=survct+consct,prob=totexp,log=TRUE)) lik } m3 = mle(fn=likfun3) p1 = 0.4214894/(1+exp(-0.3047904*(dayt-7.9255864))) p2 = 0.1926/(Day -0.25585) totexp = p1 + p2 -((p1*p2)) plot(day,percapct,ylab="Per Capita Prey Consumption",xlab="Day",main="Fig cortox Daily Per Capita Predation") points(day,totexp,col="red")

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APPENDIX B MULTIPLE COMPARISONS : CHAPTER 4 Table B-1. Results of Tukey tests of effects of Predator and Resource on A. albopictus survivorship. For interactions, differences within predator treatments (rows) are not significantly different when underlined and differences within resources (columns) are not significantly different when they share the same superscript (p < 0.01). Predator X Resource Resource Predator Low Intermediate High .04 a .16 a b .784 a None .084 a .34 a .316 b One .112 a .32 a .284 b Two .024 a .076 b .064 c Four 129

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130 Table B-2. Results of Tukey tests of effects of Predator and Resource on O. triseriatus survivorship. For interactions, differences within predator treatments (rows) are not significantly different when underlined and differences within resources (columns) are not significantly different when they share the same superscript (p < 0.01). Predator X Resource Resource Predator Low Intermediate High 0 a .008 a .06 a None .008 a .116 a .42 b One .012 a .052 a .384 b c Two .06 a .116 a .16 c Four

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131 Table B-3. Results of Tukey tests of effects of Predator and Resource on male mass of O. triseriatus adults. For interactions, differences within predator treatments (rows) are not significantly different when underlined and differences within resources (columns) are not significantly different when they share the same superscript (p < 0.01). Predator X Resource Resources Predator Low Intermediate High .116 a .163 a None none .333 a .294 a b 1 – Some treatments did not have any prey surviving to adults. One none .191 a .155 a .291 a b Two .223 a .202 a .489 b Four

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132 Table B-4. Results of Tukey tests of effects of Predator and Resource on the proportion of A. albopictus surviving. For interactions, differences within predator treatments (rows) are not significantly different when underlined and differences within resources (columns) are not significantly different when they share the same superscript (p < 0.01). Predator X Resource Resource Predator Low Intermediate High 1 a .963 a .932 a None .95 a .76 a b .444 b One .916 a .854 a .4 b Two .253 b .348 b .311 b Four

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133 Table B-5. Results of Tukey tests of effects of Predator and Resource on total prey survivorship. For interactions, differences within predator treatments (rows) are not significantly different when underlined and differences within resources (columns) are not significantly different when they share the same superscript (p < 0.01). Predator X Resource Resources Predator Low Intermediate High .02 a .084 a .422 a None .046 a .228 a .268 a One .062 a .186 a .336 a Two .042 a .096 a .112 b Four

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BIOGRAPHICAL SKETCH Marcus Griswold was born in Baltimore, MD, and later moved to the small town of New Windsor, MD. Even as a small child, he was always interested in the natural world and spent most of his time studying both aquatic and terrestrial life in nearby streams and forests. After graduating from Linganore High School, he went on to receive a degree in biology from the University of Maryland in 2000. His interest in entomology was further piqued during his work with mosquitoes at the Walter Reed Biosytematics Unit, where he learned of the diversity of habitats where mosquitoes could be found, particularly those in temporary habitats. His great interest in aquatic ecology still remains and will continue in his next degree program in environmental engineering. 152