The Pennsylvania State University

The Graduate School

Intercollege Graduate Degree Program in Ecology

THE ROLE OF HABITAT HETEROGENEITY IN POPULATION DYNAMICS:

FROM INDIVIDUAL BEHAVIOR TO METAPOPULATION STRUCTURE

A Thesis in

Ecology

By

Carrie A. Schwarz

© 2006 Carrie A Schwarz

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

May 2006 The thesis of Carrie A. Schwarz was reviewed and approved* by the following:

Ottar N. Bjørnstad Associate Professor of Entomology Thesis Co-Advisor Co- Chair of the Committee

Michael C. Saunders Professor of Entomology Thesis Co-Advisor Co-Chair of the Committee

Donald D. Davis Professor of Plant Pathology

Andrew Liebhold Adjunct Professor of Entomology Research Entomologist, Northeastern Research Station USDA Forest Service

Katriona Shea Assistant Professor of Biology

David Mortensen Professor of Weed Biology/Ecology Head of the Ecology IDGP

*Signatures are on file in the Graduate School Abstract

As human activities continue to fragment natural habitats, the need grows to be able to quantify the impacts of isolation and subdivision on population dynamics and life history strategies. Yet, most empirical and theoretical studies addressing life in a fragmented landscape leave out two sources of variation that can greatly impact predictions, results, and ultimately conservation and management decisions made. These two sources of variation are (a) variation in spatial heterogeneity experienced by different populations of the same species and (b) the inherent variation that exists between individuals making up a population. Using the forked fungus beetle, Bolitotherus cornutus, as my study organism, I looked at both empirically and theoretically the influence of habitat heterogeneity on forked fungus beetle populations from the level of life history evolution up to metapopulation dynamics. Life history traits and the role of spatial heterogeneity in the evolution of life history traits has been generally ignored, especially the influence of life history evolution on population and metapopulation dynamics.

The role of habitat patch heterogeneity on diet breadth and dispersal decisions was addressed both theoretically through the development of a dynamic state variable model, as well as empirically through the large capture mark recapture study conducted in a single host species landscape and a two host species landscape. This large data set was also used to address questions related to the maintenance of multiple male mating strategies as a function of metapopulation structure and demographic stochasticity, as well as how synchrony differs between these two landscapes. I found that for all of the questions addressed in the chapters of my thesis, habitat patch heterogeneity in the form

iii of host species greatly influences the outcomes of life history traits such as dispersal, oviposition decisions, as well as mating strategies and dispersal.

iv Table of Contents

List of Figures……………………………………………………………………………vii List of Tables……………………………………………………………………………viii Acknowledgements……………………………………………………………………….ix

Chapter 1: Introduction: Metapopulation and Spatial Ecology in General: Moving Beyond Just Numerical Response in Population Dynamics……………………..1 I. Literature Cited………………………………………………………………………...13

Chapter 2: The Landscape Ecology of Host Choice in Phytophagous Insects I. Abstract………………………………………………………………………………..18 II. Introduction…………………………………………………………………………...19 III. The Model…………………………………………………………………………...23 IV. Results……………………………………………………………………………….27 V. Discussion……………………………………………………………………….……29 VI. Literature Cited……………………………………………………………………....34 VII. Tables and Figures………………………………………………………………….37

Chapter 3: Maintenance of Alternative Male Mating Strategies in a Single Host Species and Two- Host Species Metapopulation I. Abstract………………………………………………………………………………..41 II. Introduction…………………………………………………………………………...42 III. Methods……………………………………………………………………………...46 IV. Results……………………………………………………………………………….49 V. Discussion…………………………………………………………………………….54 VI. Literature Cited……………………………………………………………………....58 VII. Tables and Figures……………………………………………………….…………61

Chapter 4: The Role of Habitat Structure in Population Synchrony: A Comparison between a Single Host- Species and a Two Host-Species Metapopulation I. Abstract………………………………………………………………………………..66 II. Introduction………………………………………………………………………….. 68 III. Methods…………………………………………………………………………….. 72 IV. Results…………………………………………………………………….. ………..75 V. Discussion………………………………………………………………….…………80 VI. Literature Cited………………………………………………………………………84 VII. Tables and Figures……………………………………………………….…………86

Chapter 5: Dispersal and Patch Connectivity as a Function of Habitat Heterogeneity and Habitat Patch Size in Metapopulations I. Abstract………………………………………………………………………………..91 II. Introduction…………………………………………………………………………...93 III. Methods……………………………………………………………………………...95 IV. Results…………………………………………………………………….…………98

v V. Discussion…………………………………………………………………………..105 VI. Literature Cited………………………………………………………………….....110 VII. Tables and Figures………………………………………………………………...113

Appendix A: Code for Chapter 2 Model……………………………………………....114

vi List of Figures

Figure 2-1. Plots of annual host quality decay curves used in the SDP model………….37

Figure 2-2. Host quality and proportion of hosts in the landscape impact breadth of host use………………………………………………………………………………..38

Figure 2-3. Host quality and proportion of hosts in the landscape impact dispersal decisions………………………………………………………………………………….39

Figure 3-1. Boxplot of the proportion of large horned and small horned males as a function of the number of females present in a local population………………………...61

Figure 3-2. Histogram of the range of local population sizes observed during the two year study period…………………………………………………………………………62

Figure 3-3. Boxplots of the proportion of large horned and small horned males as a function of julian date……………………………………………………………………63

Figure 3-4. Plots of large horned male, small horned male, and female recruitment rates as a function of time………………………………………………………………………………………64

Figure 4-1. Spline correlograms for sites R and S and for the two host species in site S………………………….…………………………………………………………..86

Figure 4-2. Plots of mean regional synchrony and 95% confidence intervals for site R, site S, and between sites R & S…………………………………………………..87

Figure 4-3. Plots of mean synchrony and 95% confidence intervals as a function of population size………………………………………………………………………...88

Figure 4-4: Recruitment rates of three sex categories over two year field season……...89

Figure 5-1. Plots of dispersal patterns between two host species in site S…………….113

Figure 5-2. Plots of dispersal patterns between two host species in site S broken down into the categories of females, all males, large horned males only, and small horned males only………………………………………………………………………………114

Figure 5-3. Histograms of dispersal distances…………………………………………115

Figure 5-4. Scatter plots of the effect of patch size on immigration and emigration…..116

Figure 5-5. Correlations between the number of patches and individual patch

vii contributes dispersers to and the number of patches that individual patch receives dispersers from………………………………………………………………………….117

viii List of Tables

Table 2-1. List of SDP model parameters………………………………………………40

Table 3-1. Recruitment model rankings for site R and site S…………………………...65

Table 4-1. Regional synchrony estimates and confidence intervals…………………….90

Table 5-1. Summary information on the number of beetles marked, sex, and dispersal………………………………………………………………………………...118

Table 5-2. Chi square values for dispersal between two host types……………………119

Table 5-3. Summary information on dispersal distance of forked fungus beetles at site R and site S…………………………………………………………………………120

Table 5-4. Summary table of linear regressions of the effect of patch size on immigration and emigration…………………………………………………………….121

Table 5-5. Summary table of the number of local populations that donate individuals to a single local population and the number of local populations that dispersers from a single population join…………………………………………………………………122

Table 5-6. Summary table of linear regressions of the correlation between the number of populations contributing dispersers to a single population and the number of populations a single population contributes dispersers to………………………………123

ix Acknowledgments

I would like to thank my committee members, Ottar Bjørnstad, Michael Saunders,

Donald Davis, Andrew Liebhold, and Katriona Shea for their support and assistance throughout my tenure as a Ph.D. student in Ecology. I am very thankful for the support given to me by my co-advisors, Ottar Bjørnstad and Michael Saunders. Both provided me with wonderful scientific advice and were wonderful mentors throughout my studies.

I wish to thank Katriona Shea for her help and input on my research, especially on the model presented in the second chapter of my thesis. Her knowledge of stochastic dynamic programming and its applications to questions of interest in my own research was invaluable.

I would like to thank all of the wonderful undergraduate students who worked with me during the summers in my field sites. Without their help and dedication the large amounts of data on which most of my research and analyses are based would not logistically have been able to be collected. Thank you so much: Mike Allen, Kris

Orndorff, Erin Fleischer, Alexandria Reichart, Sara Vanlandingham, and Michelle

McGregor. I would also like to extend a big thank you to all of the members of the lab

(past and present) who offered technical support and input into my projects: Matthew

Ferrari, Laura Pomeroy, Isabella Cattadori, Takehiko Yamanaka, Derek Johnson, Akiko

Satake, Angie Luis, and Bjørn Okland. Thank you also to the members of the Shea lab who offered constructive criticism on research and chapters: Emily Rauschert, Zeynep

Sezen, and Eelke Jongejans. Special thanks to all friends – you made the graduate school years a much more pleasant experience.

x A very big thank you to my parents, Gerardine and John Reed for their love and support throughout my life. Above all I want to thank my husband, Dietmar, and my daughter Klara: Without their support, smiles, hugs, and love none of this would be possible. You light up my life!

xi Chapter 1: Introduction Metapopulation and Spatial Ecology in General: Moving Beyond Just Numerical Response in Population Dynamics

Introduction and Motivation for the Research

As human activities continue to fragment natural habitats, the need grows to be able to quantify the impacts of isolation and subdivision on population dynamics and life history strategies. Yet, most empirical and theoretical studies addressing life in a fragmented landscape leave out two sources of variation that can greatly impact predictions, results, and ultimately conservation and management decisions made. These two sources of variation are (a) variation in spatial heterogeneity experienced by different populations of the same species and (b) the inherent variation that exists between individuals making up a population. In this synthesis chapter I will argue the important role spatial heterogeneity can play not only in population dynamics, but also on the evolution of life history strategies. In this thesis, I present evidence, both empirical and theoretical for the need to consider spatial hetereogeneity in the design of studies of population dynamics as well as life history strategies. In the case of this study, spatial heterogeneity is defined by differences in habitat patches such as size, quality and host species. Spatial ecology has made much progress in the past three decades in producing theory that predicts the dynamics of populations and two species numerical interactions.

It has not, however, had as much of an impact in other areas of population ecology, including behavioral ecology, life history evolution, and mating systems. I will begin with a general review of the inclusion of spatial ecology into studies of population dynamics and then move on to review classic metapopulation theory and extensions to the theory. I will then review the literature on the consequences of spatial heterogeneity

1 on life history strategies and mating strategies while introducing the chapters of this thesis.

The Introduction of Spatial Models to the Analyses of Population Dynamics

Driven by a lack of computational power rather than a lack of understanding of the importance of space in ecological processes, early studies of populations and simple multi-species interactions such as predator – prey and competition dynamics did not include space in the analyses of the systems’ dynamics (Nicholson & Bailey 1935,

Gurney & Nisbet 1998). Early models on population dynamics assumed that a population was closed, meaning that changes in population size were influenced only by birth and death in the absence of immigration to and emigration from other populations.

The two most common population models are the exponential population growth model and the logistic population growth model. The exponential population growth model assumes that a population grows exponentially at a constant growth rate. In the logistic model of population growth, the addition of a carrying capacity to the model caps the growth of a population relative to some resource within its environment. The dynamics generated by the discrete logistic population growth model (but not the continuous) are more diverse than those generated by the exponential growth model and can result in dynamics varying from convergence on a population size at carrying capacity, cyclic fluctuations around carrying capacity, chaotic dynamics, and extinction – all dependent on the per capita rate of increase of the population (May 1974).

2 The addition of space to these early models of population dynamics has in many

cases generated results different from those produced in the non-spatial versions. For

example, uncoupled populations exhibiting chaotic populations dynamics have been shown to sometimes produce stable dynamics when coupled via immigration and emigration (Hastings 1993). In two species systems such as competition and predator- prey dynamics, the inclusion of space into theoretical models has resulted in an expanded array of parameter space through which coexistence of both species can occur (Hassel &

May 1973, Hilborn 1975, Hastings 1978, Hassell & May 1988, Comins et al 1992).

These examples, as well as the empirical evidence supporting the important role of space in ecological processes (Murdoch et al 1989, Pacala & Hassell 1991) underline the need to consider space in ecological theory beyond just the numerical dynamics of populations.

The Role of Metapopulation Theory in Spatial Ecology and Dynamics

Metapopulation theory was first introduced to the scientific community in 1969 by Richard Levins, although similar ideas were discussed by others at earlier dates

(Wright 1940, Andrewartha & Birch 1954, MacArthur & Wilson 1963). Levins’ classic definition of a metapopulation is an assemblage of local breeding populations inhabiting distinct and discrete habitat patches that are connected to one another via dispersal of a subset of individuals. In such a system, local populations are often unstable, blinking in and out of existence, but the maintenance of the population occurs at this larger, metapopulation scale. The model developed by Levins offered a simple way of including space into the analyses of population dynamics. In this case, space is considered

3 implicitly, meaning that all local populations are connected to one another with equal probability (Hanski 1997).

4 The elegance of Levins’ theory lies in its simplicity. The classic metapopulation model provided a means for the inclusion of space into population ecology without the need for large computational power. This is both because space is modeled implicitly and the model includes only two parameters that drive the dynamics: extinction rate of local populations inhabiting habitat patches and the colonization rate of empty habitat patches. With simplicity come drawbacks, and in the case of Levins’ model these drawbacks lie in the underlying assumptions of the model. These assumptions are that all habitat patches are identical and equally connected to one another, the dynamics of local populations are in equilibrium, suggesting that there is no need to include local dynamics in the model, and the rates of colonization and extinctions are constant across all patches and over time. Computational power and the emergence of empirical evidence on how spatially segregated populations do interact allowed for the relaxation of the assumptions in the classic model and have led to a recent explosion in the development of metapopulation models.

Extensions to Levins Metapopulation Theory

The potential use of metapopulation theory in conservation and management has been the major push for the relaxation of many of the assumptions of the classic metapopulation model (Quinn & Hastings 1987, Kareiva et al 1990, Pulliam et al 1992,

Doak & Mills 1994, Nee, 1994, Thomas, 1994, Doak 1995). One of the major advances in the study of metapopulations is the inclusion of explicitly modeled space in metapopulation models (Durrett & Levin 1994). The Incidence Function Model (Hanski

1992 & 1994) provided researchers not only a tool for incorporating explicit distances between local populations and variability in habitat patch/ local population size, but also

5 a tool for linking theory and empirical data within a model framework (Eber & Brandl

1996, Hanski et al 1996, Tyre et al 2001, Moilanen & Nieminen 2002, Bonte et al 2003,

Drechsler et al 2003, Gutierrez 2005, Schultz & Crone 2005).

The addition of habitat patch heterogeneity to metapopulation models, often in

concordance with local population dynamics has also helped to synthesize theory with

empirical studies of metapopulations (Smith & Peacock 1990, Thomas & Hanski 1997).

Models of source– sink dynamics have been tied in with metapopulation structure as one case in which local populations can have very different dynamics from one another, but still function together through dispersal to enhance persistence of the metapopulation as a whole (Pulliam 1988, McPeek & Holt 1992, Roy et al 2005). Other extensions to the metapopulation model in more recent time are the addition of stochasticity in local population demography (Martinez-Abrain et al 2001, Bonsall & Hastings 2004) and environmental stochasticity (Dytham 1995, Moilanen 2004, Frank 2005)

Extensions of Metapopulation Theory Beyond Population Dynamics: The Influence of Habitat Heterogeneity on Life History Strategies and Metapopulation Dynamics

Although the majority of ecological studies incorporating a metapopulation framework have focused on dynamics at the local population and metapopulation level, some researchers have begun to incorporate metapopulation theory into life history evolution theory. The trait that most of this research has focused on is dispersal, primarily because the act of dispersal influences greatly the colonization dynamics of a metapopulation, but also because dispersal is a life history trait the greatly influences other life history traits as well. As with the numerical dynamics of populations, spatial

6 and temporal heterogeneity has the potential to influence a broad range of life history

traits from growth patterns, fecundity, sex ratio, reproductive investment, and age of mortality (Stearns 1992). The tradeoffs driving the evolution of life history traits can

vary greatly as a function of spatial and temporal heterogeneity. The result is that individuals inhabiting different local populations can vary greatly in life history strategies

depending on the aspects of the patch in which they live and the heterogeneity of those

habitat patches surrounding them. Of great importance in the metapopulation context is

how spatial heterogeneity ultimately affects metapopulation dynamics and persistence

through its influence on life history traits. In my thesis I explore the role of spatial and

temporal heterogeneity on life history traits as well as on metapopulation dynamics.

Through our studies we are able to address questions related to evolution of life history

traits in spatially and temporally variable environments. In effect, we are addressing

conservation questions with an evolutionary perspective. The chapters include not only

theoretical studies into this topic, but also focus on empirical data, which is used both to

test existing theories, but also to motivate extensions to the theory.

The Forked Fungus Beetle, Bolitotherus cornutus, is an ideal candidate for

addressing empirical and theoretical questions on the roles of spatial and temporal

heterogeneity in metapopulation dynamics and life history evolution. Previous

population genetic and dispersal studies have already shown that forked fungus beetles

exist as a metapopulation, with local population extinctions and habitat patch

colonizations driving the dynamics and persistence of the metapopulation (Whitlock

1992). Every aspect of the life cycle and life history of this beetle is closely associated

7 with the fruiting bodies of the fungal hosts. Within forests these hosts are both patchily distributed and temporally transient. In the ridge and valley region of central

Pennsylvania, forked fungus beetles inhabit two primary hosts: Ganoderma applanatum and Ganoderma tsugae. These hosts vary in densities within the forested landscape, in host quality, and in longevity. For my thesis I identified two study sites: one containing only habitat patches of G. applanatum and one containing habitat patches of both G. applanatum and G. tsugae. Using these two different landscapes as well as the two species of fungal host, I was able to investigate the effects of spatial heterogeneity on two different spatial scales: that of the metapopulation, and the local population as well. The difference in longevity of the fruiting bodies produced by these two fungal species is great. Fruiting bodies of G. applanatum may be present in a single patch for many consecutive years, while G. tsugae produces annual fruiting bodies that decay through the season. The same patch of G. tsugae may produce fruiting bodies many consecutive years, but the decay of the host through the season can change the status of its quality as a food source and overwintering source for the beetle. This is extremely important since the beetles are long lived (up to four years), are obligates on their hosts at all life stages, and do not often disperse.

Chapter 1: The Landscape Ecology of Host Choice in Phytophagous Insects

Polyphagy and oligophagy (the use of multiple food species) are common throughout the animal kingdom. Among beetles feeding upon polypore shelf fungi, for example, it is the norm rather than the exception (although some monophagous examples can be found such as Bolitophagus reticulatus). Hosts often vary in resource quality and

8 quantity spatially and temporally within a landscape, resulting in variability in the degree to which one host species is utilized relative to another. Host preference is key to herbivore population dynamics as it influences variables associated with fitness, such as survivorship and fecundity, as well as other subtler life history strategies such as migration rate.

A model of the evolution of host specialization in a source-sink metapopulation suggests that the spatial structure of habitats can have a great impact on the evolution of host specialization and/or generalization (Ronce & Kirkpatrick 2001). Another model found that spatial heterogeneity enhances coexistence probabilities of polymorphisims in habitat choice (Ravigne et al 2004). To test the influence of spatial and temporal heterogeneity on host use and dispersal patterns I constructed a dynamic state variable model using stochastic dynamic programming. The model focuses on the oviposition choices made by an individual female relative to the relative proportion of two host species within the landscape and the relative changes in quality of these two hosts throughout the season. From the outputs of this model we find that:

(a) state of the female in terms of eggs remaining to be laid and her age impact host use decisions

(b) host type on which she originates can influence host use and dispersal decisions, suggesting a mechanism for the coexistence of more than one host use strategy within the metapopulation

(c) proportion of the two hosts in the landscape and their relative quality in time greatly influence host utilization and dispersal patterns

9 Chapter 2: Maintenance of Alternative Male Mating Strategies in a Single Host Species and Two- Host Species Metapopulation

In the study of alternative mating strategies, the predictions of many theoretical models suggest that in order for two male mating strategies to coexist they must have equal fitness (Gross & Charnov 1980, Maynard-Smith 1982). Empirical evidence, however, suggests that alternative male mating strategies can exist in the absence of equal fitness (Calsbeek et al 2002). Males of forked fungus beetles exhibit pronotal horns that vary in size. Horns are used for battling over potential mates, with larger horned males showing greater success in such battles than smaller horned males (Brown &

Bartalon1986), yet small horned males still coexist with small horned males. In Chapter

2 I focus on the role of spatial structuring of local populations and local population size on the coexistence of alternative male mating strategies in the forked fungus beetle,

Bolitotherus cornutus. The results presented in this chapter are from a two year large scale capture-mark-recapture study. Patterns from the data suggest two mechanisms could be important in the maintenance and coexistence of alternative male mating strategies in forked fungus beetles:

(a) High variance in large horned male and small horned male access to females at small local population sizes

(b) early season recruitment of small horned males into local populations before recruitment of large horned males into local populations.

The results of the study suggest the important role spatial structure can play in the coexistence of alternative mating strategies. We are currently exploring this possibility further in a spatially explicit metapopulation model.

10 Chapter 3: The Role of Spatial Heterogeneity in Population Synchrony: A Comparison between a Single Host- Species and a Two Host-Species Metapopulation

By exploring regional dynamics of B. cornutus in a single versus two host species landscape, we address the role of spatial heterogeneity on regional persistence of a

species. In metapopulation theory, local population dynamics are considered to be

asynchronous. This lack of synchrony between local populations is considered an

important mechanism for the maintenance of the metapopulation. If local populations

exhibit high levels of synchrony in dynamics it can make them more prone to

synchronous extinction in response to disturbance (Hanski 1999). In chapter 3 I explore

how synchrony differs as a function of spatial heterogeneity by comparing the single host

species and two host species landscapes. Conclusions from this study are:

(a) Regional synchrony was higher in the two host species metapopulation than in the single host species metapopulation

(b) Synchrony did not decrease significantly as a function of distance between patches.

(c) Synchrony increased as a function of population size, such that larger populations were more synchronous than small populations.

Chapter 4: Dispersal and Patch Connectivity as a Function of Habitat Heterogeneity and Habitat Patch Size in Metapopulations

Although the majority of ecological studies incorporating a metapopulation framework have focused on dynamics at the local population and metapopulation level, some researchers have begun to incorporate metapopulation theory into life history evolution theory. The trait on which most of this research has focused is dispersal, primarily because the act of dispersal influences greatly the colonization dynamics of a

11 metapopulation, but also because dispersal is a life history trait the greatly influences

other life history traits as well as dynamics of local and metapopulations (Olivieri &

Gouyon 1997). McPeek and Holt (1992) found that both spatial and temporal

heterogeneity impact the rate of dispersal in a metapopulation, as well as the number of different dispersal strategies coexisting within a metapopulation. Ronce and colleagues

(2000) found that habitat structure and the level of habitat disturbance greatly impacted the degree to which dispersal was linked to another life history trait, reproductive effort,

in what is often termed a syndrome. Another dispersal model focused on the interplay

between dispersal rate and patch extinction rates (Ronce et al 2000). In general, these

dispersal evolution models suggest that dispersal is not a constant trait, and can vary in

space as a function of spatial heterogeneity, and can change in time as a function of

habitat disturbance. The outcomes of these models indicate a need for empirical metapopulation studies to look into the effects of spatial and temporal heterogeneity on dispersal strategies in metapopulations.

In chapter 4 I test the theoretical prediction that dispersal strategies can vary as a function of spatial heterogeneity. In this study compare dispersal dynamics of forked fungus beetles in the single host species metapopulation with dispersal dynamics of beetles in the two host species metapopulation. Spatial heterogeneity at the patch level and its influence on dispersal strategies was addressed in two ways: (1) how the host species on which one originates influences dispersal strategy and (2) how habitat patch size influences dispersal and overall connectivity of local beetle populations. The main conclusions for the dispersal data are:

12 (a) Proportion of the total population that disperses varies between the one host species and two host species landscapes.

(b) Host species on which one originates can influence the species of host of the new habitat patch to which one disperses.

(c) Connectivity of habitat patches was positively correlated with habitat patch size.

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Pacala, S. W. & M. P. Hassell. 1991. The persistence of host-parasitoid associations in patchy environments. II. Evaluation from field data. American Naturalist. 138: 584-605.

Pulliam, H. R. 1988. Sources, sinks, and population regulation. American Naturalist. 132: 652-661.

16 Pulliam, H. R., J. B. Dunning, & L. Liu. 1992. Population dynamics in complex landscapes: a case study. Ecological Applications. 2: 165-177.

Quinn, J. F. & A. Hastings. 1987. Extinction in subdivided habitats. Conservation Biology. 1: 198-208.

Ravigne, V., I. Olivieri, & U. Dieckmann. 2004. Implications of habitat choice for protected polymorphisms. Evolutionary Ecology Research. 6(1): 125-145.

Ronce, O., F. Perret, & I. Olivieri. 2000. Landscape dynamics and evolution of colonizer syndromes: interactions between reproductive effort and dispersal in a metapopulation. Evolutionary Ecology. 14(3): 233-260.

Ronce, O., F. Perret, & I. Olivieri. 2000. Evolutoinary stable dispersal rates do not always increase with local extinction rates. American Naturalist. 155: 485-496.

Ronce, O. & M. Kirkpatrick. 2001. When sources become sinks: migrational meltdown in heterogeneous habitats. Evolution. 55(8): 1520-1531.

Roy, M., R. D. Holt, & M. Barfield. 2005. Temporal autocorrelation can enhance persistence and abundance of metapopulations comprised of coupled sinks. American Naturalist. 166: 246-261.

Stearns, S. 1992. The Evolution of Life Histories. Oxford University Press. Oxford, UK.

Schultz, C. B. & E. E. Crone. 2005. Patch size and connectivity thresholds for butterfly habitat restoration. Conservation Biology. 19: 887-896.

Smith, A. T. & M. M. Peacock. 1990. Conspecific attraction and the determination of metapopulation colonization rates. Conservation Biology. 4: 320-323.

Thomas, C. D. 1994. Extinction, colonization, and metapopulations: environmental tracking by rare species. Conservation Biology. 8: 373-378.

Thomas, C. D. & I. Hanski. 1997. Butterfly Metapopulations. In Metapopulation Biology: Ecology, Genetics, and Evolution. Eds. Hanski, I. & M. E. Gilpin. Academic Press. San Diego, CA.

Tyre, A. J., H. P. Possingham, & D. P. Niejalke. 2001. Detecting environmental impacts on metapopulations of mound spring invertebrates – assessing an incidence function model. Environment International. 27: 225-229.

Whitlock, M. C.1992. Nonequilibrium population structure in forked fungus beetles: extinction, colonization, and the genetic variance among populations. American Naturalist. 139: 952-970.

17 Wright, S. 1940. Breeding structure of populations in relation to speciation. American Naturalist. 74: 232-248.

18

Chapter 2: The Landscape Ecology of Host Choice in Phytophagous Insects

ABSTRACT

Host choice by both herbivorous and fungivorous insects is a decision that is

impacted by more than just host preference alone. As shown by previous studies, choice can depend greatly on other factors such as density of conspecifics, and the presence of interspecific competitors and predators. We explore the implications of temporal variability in host quality and proportion of two hosts within the landscape on the ovipositioning decisions and overall host use strategy of a proovigenic female insect using stochastic dynamic programming techniques and basic metapopulation principles.

The model is motivated by the biology of the forked fungus beetle, Bolitotherus cornutus, and its two primary fungal hosts in central Pennsylvania, Ganoderma applanatum and

Ganoderma tsugae. The two hosts are patchily distributed in forested landscapes and vary remarkably in longevity. We show that host choice is dependent on the state of the female, and the decision to use one host over another depends on the quality of the hosts for larval survivorship at the time of oviposition, the proportion of the two hosts in the landscape, and the host on which the female originated. We find that female host choice and oviposition strategies can be influenced by a suite of parameters, suggesting that host preference alone will not describe most patterns of host use observed in real landscapes.

18

INTRODUCTION

Research in the area of host choice and evolution of diet breadth of phytophagous

insects has generally focused on the feeding and olfactory responses of insects to plant

characteristics, such as plant chemistry, with little integration of ecological variables, such as the temporal and spatial dynamics of host availability (Jaenike 1990). Yet recent

research suggests that spatial prevalence and arrangement of hosts in the landscape, as

well as temporal variation in host quality can play an important role in the utilization of

hosts by phytophagous insects (Rausher 1980, Thomas & Singer 1987, Horton et al 1988,

Bernays et al.1997, Kuussaari et al. 2000,). For example, Kuussari et al (2000) found

that female Glanville fritillary butterflies exhibit differences in their patterns of host use

for ovipostioning relative to the proportion of, and spatial arrangement of, different

species of host plants within their spatial network.

Field results reported in many studies reiterate this need to incorporate spatial and

temporal variability in both host presence and host quality into the understanding of host

utilization in nature. Rausher’s (1990) work on the pipevine swallowtail, Battus

philenor, is an important example of this. He found that females emerging from early

season broods distributed their eggs between two host species differently from late season

females. This difference was found to be a function of the relative abundance of the

hosts in the landscape during the two different ovipositioning periods (Rausher 1980 &

1983). In their study of colorado potato beetle, Leptinotarsa decemlineata, Horton et al

(1988) found that plant species exposure of geographically distinct beetle populations led

19 to differences in female host use as well as larval performance, with larvae performing better on plant species present in their natural geographic locations. These two studies suggest that differences in host use can arise through (a) temporal variation in host density within the same geographic location and (b) differential exposure of herbivore populations inhabiting distinct geographic locations that vary in the presence and abundance of potential host species. This and other similar examples motivate the need for research in host plant choice to incorporate spatial variation in host presence in studies.

Ecological theory has been greatly influenced over the past few decades through the incorporation of space into models of population and community dynamics (Hanski

1994, Hassell et al 1994, Bonsall & Hassell 2000, Hassell 2000, Snyder & Chesson

2004). The results of these spatial models often differ greatly from their non-spatial counterparts, especially in the parameters and range of parameters that result in stable dynamics and persistence (Snyder et al 2005). For example, systems that go extinct or showed chaotic dynamics in non-spatial models were found to display stable and persistent dynamics when spatial characteristics were incorporated. Incorporation of spatial prevalence of potential hosts promises to help expand the theory of host choice and diet breadth evolution and could explain patterns of host associations found in nature that do not necessarily match the results of laboratory studies (Singer & Parmesan 1993).

20 Metapopulation theory is a good avenue for implicitly incorporating space into

theoretical models of host choice, diet breadth, and dispersal strategies (Olivieri et al

1995, Hanski & Singer 2001, Hanski et al 2002). Using the principles of Levins’ (1969)

classic metapopulation model we assume that there are patches of host embedded within

a sea of unlivable habitat. Successful colonization of habitat patches is dependent on the probability of finding them within the landscape. By posing our research questions in a metapopulation context we can then explore how life history strategies such as diet breadth and dispersal change as a function of the proportion of available hosts in the landscape and relative host quality. This influence of spatial structure of local populations on the evolution of life history traits was termed by Olivieri and Gouyon

(1995) the metapopulation effect.

Using stochastic dynamic programming techniques, we explore how host quality, in the form of the probability of larval survivorship, proportion of host species in the

landscape, and temporal variation in host quality impact (a) diet breadth and (b) dispersal

strategies. This model is motivated by the biology of the forked fungus beetle,

Bolitotherus cornutus (Coleoptera, Tenebrionidae), and its two primary fungal hosts within the forests of central Pennsylvania, Ganoderma applanatum and Ganoderma tsugae. Forked fungus beetles are obligates on their fungal hosts, with each stage of their life-cycle tied directly to the fruiting bodies of the fungi both as a food source for adults and larvae and as the habitat in which they live. Because the beetles are long lived

(maximum lifespan four years) populations are made up of overlapping cohorts. Adults overwinter in the fungal fruiting bodies (often referred to as conks) and emerge in late

21 May / early June. Mating and ovipositioning occur soon after emergence and continue

through the summer with two large mating peaks: one soon after emergence and one

toward the end of the season, generally in mid August. Because the fungal conks are

patchily distributed within a forested landscape, dispersal between local populations of

beetles is relatively rare, but is a key force driving both local beetle population dynamics

and metapopulation persistence (Whitlock 1992).

Both of the fungal species are polypore shelf fungi, belonging to the family

Polyporaceae. G. applanatum grows on a suite of species of deciduous trees that are in

late stages of decay. G. tsugae grows on dead or decaying eastern hemlocks. The two

hosts differ in longevity with G. applanatum producing perennial fruiting bodies and G.

tsugae producing annual fruiting bodies. The distribution of these fungi in the forests is

very patchy as they require certain tree species (G. tsugae grows on eastern hemlocks and

G. applanatum on various deciduous tree species) as well as trees that are in a late stage

of decay. In some regions within the forests of central Pennsylvania only Ganoderma

applanatum is found, while in other distinct regions, where eastern hemlock grows

interspersed with deciduous trees, the two fungal host species are found growing

intermixed with one another. For a long-lived insect that is dependent on these hosts as a food source, oviposition resource, and habitat, these two very distinct landscapes, one in which only one host is available and one in which both fungal hosts are available, could greatly influence host use as well as dispersal strategies and both metapopulation and local population dynamics (McPeek & Holt 1992, Donahue et al 2003).

22

THE MODEL

In our model we explore how both (a) diet breadth and (b) dispersal strategies are impacted by the proportion of two potential hosts within the landscape and their relative changes in quality through time. In this model quality of the two hosts represents the probability of larval survivorship through the season and to adults. Because parameters of interest, such as relative host quality, proportion of two hosts in the landscape, and egg load vary through time, we developed a dynamic state variable model for addressing questions of host use and dispersal decisions. Unlike optimal foraging models based on rate maximization, dynamic state variable models assume that both the external state of the organism (its environment) and internal state (e.g. age, egg load, physiology, and behavior) are not static parameters, but rather ones that change through time (Mangel

1986, Jaenike 1990).

Dynamic state variable models are modeled using stochastic dynamic programming (SDP). Stochastic dynamic programming runs backwards in time to find optimal life histories. In a dynamic state variable model, the assumption is made that an individual behaves in a way to maximize its fitness. With this assumption the dynamic state variable model looks at how the terminal state of maximal fitness could have been achieved given the assumption that at each time step the individual will act optimally

(Mangel & Clark 1986, Mangel & Clark 1988). The model was coded in R (R

Development Core Team 2004).

23 We model the landscape implicitly as the proportion of the two host species within the landscape, following the basic metapopulation model of Levins (1969). There are two species of fungal host available for female oviposition. We assume that the host quality of the perennial species, H1, remains constant over time, while the quality of the annual species, H2, changes through time. We explore seasonality of larval survivorship on the annual host species, H2 with both a decay function in which the annual host is assumed to decrease linearly through time and a convex decay function in which the quality of the annual host increases in the beginning of the season, levels off at a high quality and then decreases in quality through the rest of the season (Fig. 1). From our field observations (Schwarz unpublished) we feel that the convex decay is the best representation of changes in host quality through the season and plan to measure the seasonal change in annual host quality in the future to compare to the two curves used in this model. Model simulations were run for multiple larval survivorship probabilities for the perennial host, H1, ranging from 0.1 (host of poorest quality and so larval survivorship low) to 0.9 (highest host quality, meaning high probability of larval survival). The parameter values used for offspring survival on the different host types as a function of their quality were bounded between 0 and 1. Model simulations were also run with varying proportions of the two host species in the landscape.

In the model, the state variable used to define the fitness function is the number of eggs the female has oviposited, x. The female is arbitrarily assumed to start out with five eggs and to be proovigenic, meaning that she cannot produce more eggs and is limited to the number of eggs she begins with (Collier et al 1994). The maximum time span of the

24 model is 10 time steps. Within each time step the individual female makes a decision of

whether to lay a single egg or not. In this model form the female is limited to only laying

a single egg if the decision to lay an egg is made. If she lays an egg she must also decide

in each time step which host to lay it on. This decision is conditional on the quality of the

two respective hosts within that time step, H1(t) and H2(t), the proportion of the annual

host, p, and perennial host, 1-p, within the landscape, current egg state of the female,

whether the female is still living in the current time step, 1-u, and the mortality cost

associated with moving, u2. Forked fungus beetles, both the males and females rarely

disperse between habitat patches. For this reason we assigned a cost to dispersal, which

is an increase in the probability of mortality within that time step. A female is assumed

to make the optimal decision in each time step based on her current state (egg load) and

the relative quality and proportion of the two hosts in the landscape. If the decision to

disperse is made, she will relocate randomly to another patch. Her host choice is

therefore directly determined by the relative proportion of the two host species in the

landscape. Space is not modeled explicitly in this model. Parameters and their

definitions are listed in Table 1. The equations describing each of the choices are as

follows:

Given that a Female is on Perennial, H1

The stochastic dynamic programming equation that defines the fitness of the

female starting on a perennial host at current state, x, and time step t is:

F(x,t) = max{ F(x, t+1)+H1*(1-u); F(x, t+1)+H1*(1-(u+u2))*(1-p); F(x, t+1)+H2(t)*(1-(u+u2))*p; F(x, t+1)+0*(1-u)}

25 In order of listing in the above equation, the first product is the fitness achieved if the

female stays on the perennial host she is currently on and lays an egg. The second in the

series defines the fitness accrued if the female decides to disperse to another patch and

lands on the same host species she is currently on, in this case the perennial host species.

The third defines the fitness accrued if the female disperses and lands on a habitat patch

of the other host species, in this case the annual, to lay an egg. The fourth part of the equation defines what happens if the female does not lay an egg in that time step and remains where she is.

Given that a Female is on Annual, H2

The stochastic dynamic programming equation that defines the fitness of the female starting on a perennial host at current state, x, and time step t is:

F(x,t)= max{F(x, t+1)+H2(t)*(1-u); F(x, t+1)+H2(t)*(1-(u+u2))*p; F(x, t+1)+H1*(1-

(u+u2))*(1-p); F(x, t+1)+0*(1-u)}

In order of listing in the above equation, the first product is the fitness achieved if the female stays on the annual host species and patch she is currently on and lays an egg.

The second in the series defines the fitness accrued if the female decides to disperse to another patch and lands on the same host species she is currently on, in this case the annual host species. The third defines the fitness accrued if the female disperses and lands on a habitat patch of the other host species, in this case the perennial, to lay an egg.

The fourth part of the equation defines what happens if the female does not lay an egg in that time step and remains where she is.

26 RESULTS

Host Quality and Presence Impact Female Host Choice Decisions

The host range of the female was found to depend not only on the quality of the two potential host species, but also on the host from which the female originated as well as the relative proportion of the two hosts within the landscape. In the first scenario (Fig

2a & 2c) we assume that the quality of the annual host decreases linearly over time, while the quality of the perennial host remains constant (Fig1). When the female originates on the annual host, the quality of the perennial host must be high and the proportion of this host in the landscape must be large relative to the proportion of the annual host in order for the female to utilize both the annual and perennial host (Fig. 2a).

In most of the scenarios, the female will use only the annual host (Fig 2a). When the female originates on the perennial host, there is a larger range of scenarios under which the utilization of both hosts can arise (Fig 2c). In general, when the proportion of the annual in the landscape is high and the quality of the perennial host is poor or mediocre, the female will oviposit on both hosts. When the quality of the perennial host is high, then the female utilizes only the perennial host even when the probability of finding an annual host in the landscape is high (Fig. 2c).

When the host quality of the annual increases and then decreases over time, as is modeled by the convex decay function, the optimal host utilization of the female is different than with a linear decrease. In this scenario when the female originates on the annual host there is a greater range of possibilities under which the female will utilize both hosts (Fig 2b). Once again, similar to the linear decay scenario (Fig. 2a), when the

27 quality of the perennial is high and the proportion of the annual host in the landscape

relatively low, the female will utilize both hosts. The utilization of both hosts also occurs

when the quality of the perennial is low but the proportion of the annual in the landscape

is also low (e.g. per host qual = 0.3 and prop annual = 0.1) as well as when the quality of

the perennial host is high, but the proportion of annuals in the landscape is also high (e.g.

per host qual =0.9 and prop annual =0.7) .

Host Quality and Presence Impact Female Dispersal Decisions

The optimal timing of dispersal and the number of dispersal events depends on the relative quality of the two hosts, the proportion of the two hosts in the landscape, and the host on which the female originated. When the quality of the annual host is assumed

to decay linearly through the season and the female starts out on a patch of the annual

host she should move only when the quality of the perennial host is high and the proportion of the annual in the landscape is low (suggesting a high probability of finding

a patch of the perennial host) (Fig 3a).

The dispersal decisions of the female vary greatly when the annual host decays

linearly but the female originates on the perennial host. In this scenario the female

should remain on the perennial and should not disperse only when the quality of the

perennial is high (0.8-0.9) (Fig. 3c). She should move once from perennial to annual if

the quality of the perennial host is low and the proportion of the annual in the landscape

is high. Interestingly, when the quality of the perennial takes on mid-range values

28 (between 0.2-0.7) there are scenarios in which the female should disperse twice during

the season: first from a perennial to an annual and then back to a perennial patch later in the season (Fig. 3c). She should make this decision if the quality of the perennial is relatively low, but the probability of finding a perennial patch again once the annual has decayed in quality is high. She moves twice also when the proportion of perennial patches is relatively low, but the quality is high enough to make it worthwhile to try to locate a perennial patch once again late in the season.

DISCUSSION

The results of this model suggest that life history strategies such as dispersal and host choice should depend on the landscape composition. In this case both the proportion of the two hosts in the landscape and the temporal changes in host quality impact both the range of hosts utilized by the female and the number of dispersal events within the season. The results also suggest that multiple life history strategies can exist within the same landscape. This idea is suggested as both host use and number of dispersal events differ between females emerging from the two different hosts, yet experiencing the same landscapes and host qualities. These results support Olivieri & Gouyon’s (1997) discussion of the metapopulation effect, in which life history strategies are impacted by

the spatial structure of suitable habitat.

29

Diet Breadth

Hanski and Singer’s (2001) study of metapopulation dynamics of Glanville fritillary butterfly and the role of host choice showed that the decision of females to colonize habitat patches was driven primarily by the host on which they fed as larvae.

We produced similar results of host use based on habitat patch type of origin in our model. In general diet breadth should depend not only on the proportion of the two hosts in the landscape, but also on the relative quality of the two hosts and the host on which the female originated. In Figure 2a in which the decay of the annual host quality is linear and the female starts off on the annual, both the quality of the perennial host and its representation in the landscape must be high in order for a female to utilize both the annual and the perennial host in that season. Interestingly, the scenario is different when the female originates on the perennial host (Fig 2c). In this case there are a greater mix of parameter values that lead to utilization of both hosts and “broader” diet. This difference, driven by host origin alone suggests that even within the same landscape females may have different host use strategies that are driven entirely by place of origin.

When the quality of the annual host is represented as a convex curve (increasing from low quality early in the season and then decreasing in quality the second half of the season) and the female originates on the annual (Fig 2b) the combination of parameters that result in the utilization of both hosts is wider. There is also an increase in the parameter combinations resulting in utilization of both hosts when the female starts on the perennial (Fig 2d). These results suggest that diet breadth is sensitive to the how the

30 quality of the annual host changes through the season. If the quality starts out at its

highest value at the beginning of the season and decreases linearly, then diet breadth is

more often restricted. If instead the quality of the annual starts relatively low and increases through the first half of the season and decreases to a low quality the second half of the season (Fig 1) then utilization of both hosts is more common than when the annual decays linearly with time.

These results show that host use, in this case in the form of ovipositioning choice, mayl vary as a function of both spatial structure and temporal variability in host quality.

The pattern of host utilization reflects tradeoffs experienced by the female. In this case,

the tradeoffs are the probability of finding the desired host when more than one host is

present and the probability of a host being of high enough quality to ensure high

offspring performance. The outcomes of this model are supported empirically in Rausher

(1980), where it was found that ovipositioning choice of female pipevine swallowtails

changed through the season in response to the relative quality of the two host species

present.

Dispersal Propensity

For organisms living within a fragmented or spatially structured landscape,

dispersal is a crucial life history trait (Wiens et al 1993). The act of dispersal connects

populations separated in space, leading to an increased probability of regional population

persistence (Hanski 1994). In this model we found that the act of dispersal as well as the

31 number of dispersal events is related to the relative quality of the two hosts over time and their relative representations within the landscape. Dispersal seldom occurs when the annual decreases linearly with time and the female originates on the annual. The female only disperses in this scenario when the quality of the perennial is high and the proportion of annuals in the landscape is low (proportion of perennials in the landscape is then high). This is because the risk associated with dispersal, that is the chance of not finding the perennial host, is low if the proportion of perennial hosts is high. If the quality of the perennial hosts is high but they are not very prevalent in the landscape the female decides to stay where she is. The risk of moving and not finding a perennial is greater than the fitness cost associated with staying and laying all eggs on the annual.

Changing the shape of the decay curve for the annual host also resulted in different dispersal scenarios.

When the annual decreases linearly but the female originates on the perennial dispersal can happen once or even twice. There are still scenarios in which not dispersing is the best choice, but there are many more scenarios in which dispersal is favored as compared to when the female originates on the annual. At intermediate combinations of perennial host quality and the proportion of annual hosts in the landscape the female should move twice: first when the quality of the annual becomes greater than the perennial and then again when the quality of the annual decreases to the quality of the perennial and below. So the female starts out on the perennial at the beginning of the season and ends up back on a perennial at the end of the season. These results fit well with the findings of Bernays et al. (1997) in their study of Shistocerca americana. In this

32 study the researchers found that the distance between types of food and the ease with which food sources could be found greatly impacted the dispersal decisions and diet breadth of S. americana individuals.

Although this model of host choice and dispersal in a metapopulation context is still a very simplistic model, the results support the basic concept that more than host preference drives host use patterns in a landscape. Parameters such as temporal variation in host quality, the host on which a female originates, and the relative representation of the different hosts within the landscape can also be crucial in driving the patterns of host use and dispersal. In the future we hope to add to this model the ability of females to vary the sizes of the clutches she lays in time, rather than just laying one egg (or clutch equivalent) per time step, as well as the impact of conspecific females on the host use and dispersal decisions of the female. Presently a female should never make the choice to disperse to a habitat patch of the same host type that she is already on. By adding the influence of conspecifics a female may choose to leave a host patch that still has a decent quality, but perhaps already too many other eggs on it, and disperse to a habitat patch of the same type.

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36 1

0.9

0.8

0.7

0.6

0.5

0.4 host quality 0.3

0.2

0.1

0 1 2 3 4 5 6 7 8 9 10

time steps

Figure 2-1: Plots of annual host quality decay curves used in the model. Dark gray line represents the linear decay function and the light gray line the convex decay function. The quality of the perennial host was held constant over time within a simulation, but was varied between simulations between quality 0.9 down to quality 0.1.

37 Host Quality and Proportion of Hosts in the Landscape Impact Breadth of Host Use

0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig 2-2a Fig 2-2b

0.9 0.9 0.8 0.8 0.7 0.7

Proportion of Annual Host in the Landscape 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig 2-2c Fig 2-2d

Quality of the Perennial Host

BOTH HOSTS ANNUAL ONLY PERENNIAL ONLY

Figs 2-2: Fig 2-2a- Quality of annual host decreases linearly through time and female starts on the annual host. Fig 2-2b - Quality of annual host increases then decreases through time and the female starts on the annual host. Fig 2-2c-Quality of annual host decreases linearly through time and female starts on perennial host. Fig 2-2d - Quality of annual host increases then decreases through time and the female starts on the perennial host.

38 Host Quality and Proportion of Hosts in the Landscape Impact Dispersal Decisions

0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig 2-3a Fig 2-3b

0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5

Proportion of Annual Host in the Landscape 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig 2-3c Fig 2-3d

Quality of the Perennial Host

NEVER MOVE MOVE ONCE MOVE TWICE

Figs 2-3: Fig 2-3a-Quality of host decreases linearly through time and female starts on the annual host. Fig 2-3b-Quality of annual host increases then decreases through time and female starts on annual host. Fig 2-3c-Quality of annual host decreases linearly through time and female starts on the perennial host. Fig 2-3d- Quality of annual host increases then decreases through time and the female starts on the perennial host.

39 Table 2-1: List of Model Parameters

Parameter Meaning Values Used H1 Larval Survivorship on Host 1 Between 0.1 and 0.9

H2 Larval Survivorship on Host 2 Decreased over time steps u Female’s probability of dying 0.05 u2 Probability of dying while dispersing 0.0.5 p Proportion of Host 2 in the landscape Between 0.1 and 0.9

40 Chapter 3: Maintenance of Alternative Male Mating Strategies in a Single Host Species and Two- Host Species Metapopulation

ABSTRACT

Alternative male mating strategies are a common phenomenon in many sexually

dimorphic species. The maintenance of multiple mating strategies within populations has

been a major area of both theoretical and empirical research in the subject of mating

systems. Although general theory suggests that maintenance of multiple strategies can

occur only when both strategies have average equal fitness, the results of many empirical

studies suggest coexistence can occur without equal fitness. In this study of the forked

fungus beetle, Bolitotherus cornutus, we use two years of capture-mark-recapture data to help distinguish potential mechanisms for the maintenance of both the large horned male

morphs and small horned male morphs within their populations. Results suggest that

both high variance in access to females at small population sizes as well as early

recruitment of small males into local populations could be mechanisms allowing for the

coexistence of small horned males with the more dominant large horned males.

41 INTRODUCTION

In animal species exhibiting sexual dimorphism it is commonplace for polymorphisms to exist within the sex exhibiting the character. These polymorphic characters are often physical characters, such as size, horns or colorations of plumage

(Peek 1972, Deutsch et al 1990), but they can also be behavioral characters such as differences in calling behaviors exhibited between males of some frog species (Ryan

1983, Ryan 1988, Ryan & Wilczynski 1988). General theory on sexual selection and mating strategies often explores how polymorphisms of a trait can exist in the presence of sexual selection either by the other sex or through competition within one of the sexes.

How do morphs whose traits are not under positive selection persist within the population? One of the few theoretical conclusions is the persistence of alternative mating strategies can arise when certain conditions favor one morph and other conditions favor the other morph that is “preferred” (Dawkins, 1980). In order for this to occur, however, theory suggests that the average fitness of these morphs must be the same or coexistence cannot occur (Gross & Charnov 1980, Maynard-Smith 1982).

In the case where average fitness of morphs must be equal, the theoretical predictions often do not fit well with the results of empirical research. In many field studies for variety of species exhibiting polymorphic male mating strategies, coexistence has been found in the absence of evidence of equal fitness between the mating types

(Gross 1996, Sinervo & Lively 1996). Two alternative mechanisms for coexistence of

multiple strategies have since been suggested. Eberhard (1982) proposed a

developmental mechanism that would allow for alternative strategies when certain

42 environmental cues, such as food resources, temperature, or light reach a switching

threshold. Empirical research on the ranges of body size, coloration, and dispersal

strategies in sexually dimorphic species supports the proposal (Messina & Renwick 1985,

Fischer & Fiedler 2001). Other empirical work has shown that such environmental cues in some species of insects can result in the early emergence of some individuals who skip developmental larval instars. These individuals, though generally smaller than their counterparts which go through all larval instars, may have the advantage of being able to mate with females prior to emergence of larger, dominant males (Bell 1994).

A second mechanism for coexistence proposed by Shuster and Wade (2003) is that mating success has a large variance. Most theoretical models of alternative mating strategies are based in game theory, which considers only the average reproductive success of the various mating morphs/ strategies (Maynard Smith 1982). Under such conditions it is rare for the coexistence of two strategies to occur. Shuster and Wade propose that in situations where the variance in mating success is high, alternative mating strategies of unequal average fitness can coexist (Shuster & Wade 1991), which has been backed in studies such as Calsbeek and collaborators’ (2002) work on the side blotched lizard. They found that the male mating strategy of “usurper” had a greater variation in fitness compared to the other mating strategy of “defender”, and that this high variability in fitness permits coexistence of mating strategies. In their study of garter snakes, Shine et al. (2005) also found that alternative mating tactics of male garter snakes had differential success depending on population size and general stochasticity associated with locating female mates away from denning areas.

43

In this study we examine the role of metapopulation structure in the maintenance

of alternative male mating strategies. The inherent spatial structure of a metapopulation,

coupled with the small local population sizes make them an excellent system for

addressing questions related to reproductive variance and coexistence of alternative

mating strategies. The forked fungus beetle, Bolitotherus cornutus, is an ideal organism

for studying the effects that multiple small local populations within a landscape have on

the maintenance of alternative mating strategies. Forked fungus beetles inhabit a patchily

distributed resource, the fungal fruiting bodies of a limited range of species in the Genus

Ganoderma (Heatwole 1968). The majority of beetles do not disperse between local

populations occupying different habitat patches and those that do disperse rarely do so

more than once in their lifetime. Similar to the theoretical basis of the metapopulation

concept (Hanski 1989), local populations of forked fungus beetles experience extinction

and colonization of new patches through limited dispersal, resulting in maintenance of

the population at the metapopulation, rather than local population level (Whitlock 1992).

Bolitotherus cornutus is a sexually dimorphic beetle in the family Tenebrionidae.

Males exhibit pronotal horns, the length of which is positively correlated with body size

(Conner, 1988). Horns are used for ritualized battling over potential mates, with larger

horned males showing greater success in such battles than smaller horned males (Brown

& Bartalon, 1986). The high success rate of larger horned males in competition for

female mates begs the question of how small horned males are able to persist if they are outcompeted by the larger horned males? One potential mechanism proposed by Conner

44 (1989) is that sexual selection on horn size in B. cornutus is a density dependent

phenomenon. He found that at small population sizes (number of males between 9-29) access to females by small horned males was reduced by the presence of large horned males. At higher densities (number of males between 32-51) large horned male access to

females was not as high as at lower densities, allowing for the possibility of more small

horned matings with females.

In this paper we propose two other mechanisms that could also be important in the

maintenance of the small horned male morphs. The first of these is demographic

stochasticity. The spatial structure of B. cornutus populations in nature consists of many

small, local populations and some large local populations. Over two years study on most

observation dates, the counts of adults beetles in local populations were heavily skewed

to between 1 and 5 individuals. Within these small populations, there were many

occasions in which females were present with only small horned males, as well as times

in which females were only present with large horned males. This high variance in

access to females by male morphs suggests that very small population sizes allow small

males more of a chance at mating success. Analysis of capture-mark-recapture data also

suggest a potential second mechanism for maintenance of the small horned male morphs.

Results suggest that small horned males often recruit earlier in the season than large males at a time when females are already present. This early recruitment mechanism fits in well with the theories of developmental threshold suggested by Eberhard (1982).

45 METHODS

Establishment of Study Sites

We identified two distinct locations within Rothrock State Forest, Centre County,

PA, one site containing only the perennial host species Ganoderma applanatum, and

another site which had two host species: G. applanatum and the annual host species

Ganoderma tsugae. Because the tree host species utilized by G. applanatum are

deciduous, we identified a large area in which only deciduous trees were found. Eastern

hemlock, which is the host of G. tsugae, is found in the ridge and valley region of central

Pennsylvania.

Within the boundaries of each site all potential habitat patches were located and

geo-referenced. We used the same methodology for defining a B. cornutus habitat patch

as has been used by previous researchers working with the beetle (Whitlock 1992). As

such, a patch is represented by all fungal fruiting bodies found growing on the same log

or series of logs whose bark was touching (e.g. any logs that had fallen down on top of

one another). Each patch was given a distinct identification number and within each

patch all fruiting bodies were given unique identifiers. Data on the species of the fungus

in each patch, the number of fungal conks, and the size of each fungal conk within each

patch were recorded.

46 Capture-Mark-Recpature Methods

Within each of these two study sites we located between 32 and 46 patches

potentially holding local populations of B. cornutus. Upon location, we geo-referenced

each of the patches, recorded information about each patch – including species of host,

the number of fungal conks within the patch, and the size of each of the fungal conks.

Each patch was visited every two weeks between May and October of 2003 and 2004. At each visitation we collected data on individuals of B.cornutus. Each beetle located was

given a unique marking so that individuals could be followed over time and information on survivorship, recruitment and dispersal could be gathered. Upon each visitation, we

recorded the number of newly marked individuals on that date, the number of previously

marked individuals present, and the location of each of those individuals in terms of

patch number and within-patch conk number.

The sex of each individual was recorded, and body length (mm) was recorded.

Because of the curvature of the horns on the male beetles we did not measure horn length

in the field and instead divided horn length into two categories: large horn individuals

and small horn individuals. If the length of the horns extended beyond the end of the beetle’s head it was recorded as a large horned male. If horns did not extend beyond the end of the head it was recorded as a small horned male. A total of 1955 beetles were individually marked and followed between May 2003 and October 2004 in the two study sites.

47 Estimating Survivorship and Recruitment

We analyzed the capture history data from the two sites using Program Mark

(White & Burnham 1999). Because we were interested in estimating both survivorship

and recruitment we used the Pradel model (Pradel 1996, Nichols et al 2000). Capture

history data gathered for each beetles was transformed into a series of 1’s and 0’s for each of the observation time periods. 1’s indicated that the individual was observed on that particular date, while 0’s indicated that the individual was not observed on that particular observation date. We were interested in estimating recruitment rate for females, large horned males, and small horned males separately, so data were formatted such that sex category was included in the analysis. For the two host species site S, we also formatted the capture history data by patch host species as well. This allowed us to see if recruitment or survivorship varied between the two host species, the perennial, G. applanatum and the annual, G. tsugae. Within Program Mark we ran a series of parsimonious models in order to test the dependence of survivorship and recruitment on

(a) sex category, (b) host species, and (c) time. Models were tested against the data and ranked in order of best fit in Program Mark using Akaike’s Information Criterion (AIC).

Estimating Minimum Population Sizes and Operational Sex Ratios for Data Analyses

Because not all beetles are observed on the fungal fruiting bodies or bark of the trees at all times, and because larvae remain within the fruiting bodies and are not observable in the field, we used minimum number alive as a surrogate for population size. In this study minimum number alive is defined as all observed adult B. cornutus on the fruiting bodies or bark of the tree and was used as a surrogate for population size due

48 to observational bias. We recorded minimum number alive for each patch on each

observation day over the two year study. Because this study is focused on alternative

mating strategies, and we are interested in those adults that are present on the fruiting bodies because they are the ones most likely to be actively mating or searching for a mate, this measure of minimum population size should be adequate. Operational sex ratio was calculated directly from minimum number alive values as the proportion of observed females and the proportion of observed males. The proportion of both male horn categories were also calculated from the minimum number alive data.

RESULTS

The role of small population sizes in the maintenance of alternative male mating strategies

High variance in the presence of small horned and large horned males at small population sizes was found. Figure 1 depicts the proportion of small horned and large horned males as a function of the number of females. In these plots we used minimum female abundance since females are the resource of interest for mating purposes.

Minimum female abundance, similar to minimum population size, is the number of females observed at a particular patch on a particular sampling day. The proportion of small males is calculated by dividing the minimum number of small males observed at a particular patch on a particular sampling date by the total number of males observed on that patch and sampling date. Figures 1a-h all show that at small abundances of females, the proportion of small males and large males is highly variable regardless of site or host

species. This large variability in abundance of the two male morphs at small population

49 sizes is one potential mechanism for the maintenance of the two male morphs. When

viewed in conjunction with the histograms in Figure 2a-b, which show that most

observed populations contained between 1 and 5 individuals, the results more strongly

suggest the potential importance of multiple small populations in the maintenance of

alternative male mating strategies.

In the single host site, which contained the perennial fruiting body fungal species,

G. applanatum, the high variance in the proportion of large and small male morphs remains high until the number of females increases to around 13. At this point the proportion of the two mating strategies stabilizes at around 0.8 for large horned males

and 0.2 for small horned males. At these higher female abundances the number of

populations is much smaller, and this limited sample size could cause a problem in

interpretation of the proportion of the two categories of male morphs, but it nevertheless

stabilizes over many larger abundances, to suggest a real effect.

In the two host site, which contained both the annual and perennial fruiting body

fungal species, G. tsugae and G. applanatum, Figures 1c and 1d suggest that overall the

proportion of large males and small males is about equal at 0.5. Again, the results are

highly variable when the abundance of females is small, but an overall average of 50% of

both male morph categories dominates. Figures 1e and 1f are the observed populations

on only the perennial host patches, G. applanatum, in the two-host site. These two

figures are very similar to Figures 1c and 1d, which depict the boxplot results for the

whole two host site. Figures 1g and 1h are all of the observed populations on only the

50 annual host patches. Here, one can see that the abundances of B. cornutus do not get as large as those observed on the perennial host. The variance in proportion of small horned and large horned males is again high at low female abundances, but interestingly it remains high even as female abundance increases. These results suggest that the alternative host, the annual, G. tsugae, could also be important in the overall maintenance of the small male morphs. These results imply that the landscape available to B. cornutus, whether it is a single host or multiple host landscape could have great implications on the maintenance and relative abundance of alternative mating strategies.

The role of differences in recruitment timing on the maintenance of alternative male mating strategies

Recruitment is an estimate of the number of new individuals that enter a population between two time steps. In our study we compared recruitment rates between the three sex categories: females, small horned males, and large horned males. Small horned males were found to recruit into populations earlier in the season than large horned males. Plots of the proportion of small horned and large horned males as a function of julian date (Fig 3a –h) support the idea of early-season access to females for small horned males during a period of absence from competitive pressure from large horned males. For the single host site (Figs 3a and 3b), there is an approximate one and a half to two week window in which small horned male representation within the metapopulation is greater than large horned males. Within the two host species site the pattern is different. Figures 3c and 3d suggest that both male categories are present early in the season. When looking at the breakdown of the data as a function of host species,

51 we see that on patches of the annual host species the presence of small males at higher proportions than large horned males early in the season is maintained for even longer than observed in the single host species site. On the annual patches small horned males remain at a higher abundance than large horned males for over a month. On the patches of the perennial host species average proportion of small horned and large horned males remains at approximately 50/50 throughout the season.

Tables 1a and 1b report the model ranking results from Program Mark. The model with the lowest AIC value appears first in the model list and using AIC methods is the model found to best fit the data. The model chosen to best fit the data indicates that rates of recruitment differ temporally between the three sex categories (female, large horned males, and small horned males), as well as between fungal host species. In the single host species site (Table 1a), the model with the lowest AIC ranking has recruitment as both sex category and time dependent. In the two host species site (Table

1b) the model with the lowest AIC ranking has recruitment as sex category, host species, and time dependent (Table 1b). Estimates of survivorship derived from the best fit models suggest that its role in the dynamics of B. cornutus is not as great as the role of recruitment in their population dynamics.

Plots of recruitment rates in the single host site over time (Figure 4a) suggest that small horned males have a higher recruitment rate than large horned early in the season.

When normalized by the representation of each of the sex category groups within the population, (Figure 4b) we see a large spike in the number of small males early in the

52 season before the spike in the abundance of large males. Females show the earliest spike

in abundance, which suggests the potential for early season matings between small

horned males and females before the abundance of large horned males increases.

Figures 4c through 4f show the estimated recruitment rates for beetles on patches of the perennial host (4c) and patches of the annual host (4e). The results suggest that

small horned males recruit earlier in the season on the perennial patches than the large

horned males, but that large horned males have high recruitment rates as early in the

season on the annual patches as the small horned males. These results suggest that the

pattern exhibited in Figures 3g an 3h is the result of older males emerging from

overwintering. There is a large difference in representation of small horned males and

large horned males early in the season, with small horned males having a larger

representation early in the season than large horned males on the annual host patches.

Yet recruitment of new individuals into the populations does not exhibit a similar pattern.

For this reason we believe the difference in representation of large horned males and small horned males early in the season on the annual host patches is a result of the

presence of older small horned males emerging from overwintering as adults.

53 DISCUSSION

The results of this capture mark recapture study suggest there could be more than one mechanism aiding in the maintenance of alternative male mating strategies in the forked fungus beetle, Bolitotherus cornutus. First and foremost is the inherent demographic stochasticity associated with existing in a metapopulation made up of many small local populations. Small population sizes, especially those between 1 and 5 individuals provided many occasions for small horned males to have sole access to females. At these small population sizes there was no difference between the probability of large horned males being alone with females and the probability of small horned males being alone with females. These scenarios offer small horned males opportunities to find females and mate in the absence of high competition from large horned males. Similar results were found by Calsbeek et al (2002) in their side blotched lizard study.

Differences in the presence of small horned males to large horned males early in the season could offer another mechanism by which small horned males can successfully mate with reduced competition from large horned males. Figures 3a – 3h suggest that small horned males are represented in higher proportions early in the season than large horned males. This result fits well with Eberhard’s (1982) idea that certain environmental or genetic cues can activate developmental switches in some individuals in a population and lead to rapid growth or the skipping of a larval instar, with the end result of earlier emergence time.

54 The results of the recruitment data represented in Figures 4a-4f suggest that in the

single host site these differences in representation of the two male morph categories early

in the season, with small horned males showing higher representation in the early

populations and higher recruitment of new individuals early in the season, is in part

driven by recruitment of new small horned individuals into the population. Similar

results are seen when considering only the recruitment rates in the perennial host patches

of the two host site. These patches are comprised of the same species as all of the

patches in the single host site, G. applanatum. Early season recruitment patterns differ in

the annual host, G. tsugae. Here recruitment rates are high for both male morph

categories early in the season, yet small small horned males are represented at higher

proportions early in the season on the annual host patches. This suggests that older small

horned males make up the majority of the small horned males present early in the season.

These results point to an interesting possibility: that different landscapes could produce different mechanisms for maintenance of alternative male strategies and the abundances at which these two strategies exist. Theoretical work on life history traits such as dispersal and reproductive output support this idea (Kaitala et al 1989). With regards to life history strategies, this phenomenon has been given the name “the metapopulation effect” (Olivieri & Gouyon 1997). The outcomes also fit well with general theory regarding multiple species coexistence and maintenance of biodiversity, in particular the principles of competition-colonization tradeoffs (Chesson 1990, Snyder &

Chesson 2004), the storage effect (Warner & Chesson 1985), and relative nonlinearity

(Chesson 2000). The existence of B.cornutus in small, local populations connected by

55 dispersal of individuals can act to buffer the competitive effects of large horned males on

the smaller horned males. Small population sizes then provide the possibility for small horned males to successfully mate in the absence of large horned males.

One of the main principles of the storage effect is that species specific responses to the surrounding environment (Chesson 2003) can also play a role in the maintenance of multiple mating systems within a single species. As the results of this study suggest, small horned males tend to recruit into the existing beetle populations earlier in the season than the large horned males. This early emergence response is probably a result of both differential sensitivity to the accumulation of degree days between developing males and the host species in which development occurs. Relative nonlinearity is a coexistence mechanism in which the degree of competition varies in space and time, combined with varied nonlinear responses to competition between the two competing species. Our study, combined with the results of Conner’s work (1988 & 1989 ) suggest that mating success, which in this case is determined by competition, is related nonlinearly to population density. The potential for mating success of small males is high at very small densities, low at intermediate densities, and again higher at large population densities.

The results we obtained differ from the results of Conner’s (1989) study of density dependent sexual selection in B. cornutus in that the smallest populations offer the greatest potential for successful mating of small horned males through high variance in the proportions of the two male mating types at these low abundances. This apparent

56 discrepancy may not necessarily be real. The small population sizes in our study are

much smaller than those in Conner’s study. It could very well be that at the sizes of

populations he followed, large horned males are very successful at out-competing small horned males because both are always present.

The results presented in this study do not include direct observation of mating

success of either of the male categories or reproductive output of females post mating,

but previous natural history work with B. cornutus has shown that they are actively

mating throughout the season (Connor 1988) and that females do not reject mating

attempts made by smaller horned males – they are just out-competed by larger horned

males. In the future, studies of mating success similar to those conducted by Conner

(1989) would be needed to better understand how very small population sizes may

equalize mating success of small horned and large horned males. The results do,

however, suggest that the inherent spatial structure of metapopulations, and small

population sizes of relatively immobile species could offer alternative mechanisms for

the maintenance of multiple male mating strategies.

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Whitlock, M. C. 1992. Nonequilibrium population structure in forked fungus beetles: extinction, colonization, and the genetic variance among populations. The American Naturalist. Vol 139 No. 5. 952-970.

60 3-1a: Proportion Small Males in Single Host Site R 3-1b: Proportion of Small Males in Two Host Site S as a Function of Number of Females as a Function of Number of Females

3-1c: Proportion of Small Males on Perennial Patches 3-1d: Proportion of Small Males on Annual Patches Site S as a Function of Number of Females Site S as a Function of Number of Females Proportion Small Males

Number of Females Present

Figure 3-1a-d: Boxplot of the proportion of small horned males as a function of the number of females present in the local population. Fig 3-1a represents the single host species site, site R. Fig 3-1b represents all of the local populations in the two host species site, site S. Fig 3-1c represents local populations on only the perennial host species, Ganoderma applanatum. Fig 3-1d represent local populations on only the annual host species, Ganoderma tsugae.

61 3-2a: Local Population Sizes in the One Host Species Site, Site R

140

120

100

80

60 frequency 40

20

0 1to5 6to10 11to15 16to20 21to25 26to30 31to35 36to40 41to45 1 to 5 6 to 10 11 to 15 16 to20 21 to 25 26 to 30 31 to 35 36 to 40 41 to 45

local population size

3-2a: Local Population Sizes in the Two Host Species Site, Site S

200

180

160

140

120

100 frequency 80

60

40

20

0 1 2 3 4 5 6 7 1 to 5 6 to 10 11 to 15 16 to 20 21 to 25 26 to 30 31 to 35

local population size

Figure 3-2a-b: Histogram of range of local population sizes observed during th two year study period. Site R is the single host species site and is represented in Fig 3-2a. SIte S is the two host species site and is represented in Fig 3-2b. The dark gray lines are local populations observed on patches of the perennial host species, Ganoderma applanatum. The light gray lines are local populations observed on patches of the annual host species, Ganoderma tsugae.

62 Fig 3-3a: Single Host Site: Small Male- Julian Date Fig 3-3b: Two Host Site All Patches: Small Male- Julian Date

Julian Date Julian Date

Fig 3-3c: Two Host Site Annual Patches Only Fig 3-3d: Two Host Site Perennial Patches Only; Small Male Small Male proportion of small males

Julian Date Julian Date

Figure 3-3a-d: Boxplots of proportion of large horned males and small horned males as a function of Julian Date. Fig 3-3a represents the single host species site, site R. Fig 3-3b represents all of the local populations on the two host species site, site S. Fig 3-3c represents local populations on patches of only the perennial host species, Ganoderma applanatum. Fig 3-3d represents local populations on patches of only the annual host species, Ganoderma tsugae,

63 Fig 3-4a: Recruitment Rates One Host Site: Site R Fig 3-4b: Normalized Recruitment Rates One Host SIte: Site R 1 70

0.9 60 0.8

0.7 50

0.6 40 0.5 30 0.4

0.3 20 0.2 10 0.1

0 0 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16

date (every two weeks over two sampling years)

Fig 3-4c: Recruitment Rates Two Host Site: Fig 3-4d: Normalized Recruitment Rates Two Host Site:

Perennial Patches Only, Site S 60 1 Perennial Patches Only, SIte S

50 0.8

40 0.6

30 0.4

20

0.2

10

0 0 5 10 15 20 0 0 2 4 6 8 10 12 14 16 18 20

date (every two weeks over two sampling years)

Fig 3-4e: Recruitment Rates Two Host Site: Fig 3-4f: Normalized Recruitment Rates Two Host Site

1 Annual Patches Only, Site S Annual Patches Only, Site S 40

35 0.8 30

0.6 25

20 0.4 15

10 0.2

5

0 0 0 2 4 6 8 10 12 14 16 18 20 0 5 10 15 20

date (every two weeks over two sampling years)

Fig 3-4a-4f: Plots of recruitment rates over time estimated using Program Mark (Figs 4a, 4c, 4e) as well as plots of recruitment rates normalized by the representation of the three sex categories over time. Figs 3-4a & 3-4b represent the single host species site, site R. Female Figs 3-4c & 3-4d represent recruitment on the perrenial host patches, Large Horned Male Ganoderma applanatum, in the two host species site, site S. Figs 3-4e & 3-4f represent recruitment on the annual host patches, Small Horned Male Ganoderma tsugae, in the two host species site, site S.

64 Site R Site S median min max median min max All 29.07 1.41 211.40 4.00 550.81 Dispersers Female 1.41 1.41 170.66 32.8 4.00 550.81

Lg Males 54.33 1.41 196.49 27.31 5.10 550.81

Sm Males 1.41 1.41 211.40 47.42 9.22 503.61

Table 5-3: Summary information on the dispersal distances (meters) of forked fungus beetles in the single host species landscape, site R, and the two host species landscape, site S. Median distance dispersed, as well as minimum and maximum distances are reported. The categories represented in the table are all beetles observed to have dispersed, all female dispersers, all large horned male dispersers, and all small horned male dispersers.

120 CHAPTER 4: The Role of Habitat Structure in Life History Synchrony: A Comparison between a Single Host- Species and a Two Host-Species Metapopulation

ABSTRACT

Population synchrony is a common phenomenon in studies of fluctuations in

population dynamics in space and time. The degree of synchrony and the distance over

which synchrony is maintained can have a great influence on how local populations

interact and on how long populations at both the local and regional scale persist.

Metapopulation theory suggests that high levels of synchrony between spatially separate

local populations can result in higher rates of extinction for the entire metapopulation.

The primary reason for a higher extinction rate at high synchrony between local

populations is that all populations fluctuate in a similar fashion. All will respond

similarly to density independent effects of weather and climate or random catastrophic

events experienced by the entire metapopulation. In this study we investigate the role of

habitat heterogeneity on synchrony between local populations in a metapopulation. We

conducted a two-year study of forked fungus beetle, Bolitotherus cornutus,

metapopulations. One of these metapopulations contained habitat patches of only one

species of fungal host, Ganoderma applanatum, while the other metapopulation had

habitat patches of G. applanatum as well as habitat patches of a second host species,

Ganoderma tsugae. We found that regional synchrony was higher in the two-host

metapopulation than in the single host metapopulation. Synchrony did not decrease

significantly as a function of distance between patches in the two metapopulations,

although a slight trend in year 2 at the two-host site suggests that synchrony does

decrease at shorter distances, but then increases again at the regional scale. The results

66 from this study indicate the importance of spatial heterogeneity of hosts on the degree of synchrony between local populations within a metapopulation. We found that metapopulations of the same species can exhibit different degrees of synchrony. As the incorporation of metapopulation theory into applied conservation increases, it will be beneficial to determine the degree of synchrony within separate metapopulations rather than to assume all metapopulations of the same species experience similar levels of regional synchrony.

67

INTRODUCTION

In population ecology, the term synchrony refers to the degree to which two or more populations change in the same fashion across time and/or space. There are multiple methods for measuring synchrony, but the most common methods compare fluctuations in abundance between two or more populations or the growth rate of multiple populations over time (Bjornstad et al. 1999; Buonaccorsi et al. 2001). The study populations often inhabit the same spatial location, as is common in interspecies synchrony studies. The classic example of such studies being Elton’s initial work on the synchrony between a predator, the Canadian lynx, and its prey, the snowshoe hare

(Hudson and Cattadori, 1999). Quite often, however, studies of synchrony focus on a single species. In this case the degree of synchrony is a measure made between spatially separated populations of the same species (Buonaccorsi et al 2001).

Synchrony among spatially divided populations most often results from three possible mechanisms: (a) the dispersal of individuals among populations (Engen et al

2002, Ives et al 2004), (b) correlation in environmental factors across space synchronizing populations experiencing similar dynamics – often referred to as the

Moran Effect (Hudson & Cattadori), and (c) the effects of mobile natural enemies (Singh et al. 2004, van Nouhuys & Lei, 2004). The degree to which these three mechanisms act in synchronizing the dynamics of spatially segregated populations is an important question in population and community ecology, and one to which a gradient of answers exist.

68 The degree of synchrony expressed between spatially separated populations and the distance to which spatial correlation extends are important components for understanding the extent to which spatially separate populations are linked to one another. This information is important not only for enhancing the general theory of spatial population dynamics, but also for very applied questions related to conservation and pest management.

In the context of metapopulation theory, high regional synchrony of local population dynamics can drastically decrease the persistence time of the metapopulation

(Matter 2001). For example, Bergman and colleagues (2004) found in their study of a butterfly metapopulation in Sweden that synchrony between local populations would result in an increase in the risk of extinction for the metapopulation. Similarly, Reed

(2004) found in a theoretical model that environmental correlations between local populations led to reduction in the persistence of the metapopulation. Through a series of spatially explicit models, Johst and Drechsler (2003) found that the effect of synchrony on metapopulation persistence was negative when correlated environmental disturbance led to synchronous increases in abundance of remaining local populations, but if the disturbance resulted in the clustering of occupied habitat patches, persistence time of the metapopulation increased.

The concept that synchrony between local populations in a metapopulation increases risk of extinction is confounded by the type of metapopulation in question.

Singh and colleagues (2004) found that the degree of regional synchrony exhibited by a

69 metapopulation varied between a homogenous and heterogeneous landscape. In this

study, homogeneity led to higher regional synchrony, while heterogeneity led to

asynchrony between local populations. In their study of source – sink metapopulations,

Gonzalez and Holt (2002) found that in a metapopulation made up of both local

populations acting as sources as well as local populations acting as sinks, regional

synchrony increased persistence when dispersal was high relative to the rate of

population decrease in the sinks. As a growing number of conservation plans base their

methodologies on metapopulation theory, it is important to define the structure of the

metapopulation in question and to understand the degree of synchrony that exists between

local populations before a strategic management plan is put into effect.

In this study we investigated the role of host spatial structure and host type on the

synchrony of local populations in metapopulations of the forked fungus beetle,

Bolitotherus cornutus. In the ridge and valley region of central Pennsylvania, there are

two primary fungal hosts fed upon and lived within by B. cornutus, Ganoderma

applanatum and Ganoderma tsugae. These two hosts vary greatly in their presence in the

landscape as well as their longevity relative to that of B. cornutus. G. applanatum, a

polypore shelf fungus that produces perennial fruiting bodies, can be found growing on many species of deciduous trees in a state of decay within the eastern United States. G. tsugae, on the other hand, produces annual fruiting bodies and has a more limited host range, growing only on decaying eastern hemlocks in the eastern United States (Carlile et al 2001). We identified two locations for study, one in which only patches of G.

70 applanatum were found, and a second site in which both of these host species, G. applanatum and G. tsugae, were found together in the landscape.

Unlike the results of Singh et al (2004), we expect that synchrony will be higher in the two host species metapopulation than in the single host species metapopulation.

We expect heterogeneity, in this case, to synchronize the dynamics across local populations because of the seasonality associated with G. tsugae. This host species produces annual fruiting bodies. The fruiting bodies decay through the summer and most fall off of the hemlocks by the end of the season. For a beetle that lives up to four years

(Liles 1956) this resource, even if it is a good food source early in the season, is rather ephemeral and not as predictable a resource for overwintering.

We hypothesize that the seasonality of this annual host species and dispersal between local populations synchronizes the dynamics of local populations across the two- host metapopulation. Because the longevity of the perennial fungal host, G. applanatum, is dictated by the availability of resources in the decaying tree on which they are growing, turnover of perennial patches is not expressed as a seasonal phenomenon across habitat patches as is the case for the annual host, G. tsugae. For this reason, we expect local populations of forked fungus beetles within the single host dynamics to exhibit less synchronous dynamics than in the two-host landscape.

71 METHODS

In order to look at the role of habitat heterogeneity on both synchrony of local beetle dynamics and dispersal between local populations, we located two main study sites within Rothrock State Forest, Centre County Pennsylvania. The first of these two sites,

Site R, contains only one of the species of fungal host utilized by B. cornutus. The fungal host present at this site is G. applanatum, which produces perennial fruiting bodies on a variety of deciduous tree species. Because the resource is perennial, it offers the beetles a stable habitat in which to live and complete its lifecycle. This is of importance for B. cornutus since dispersal is limited within this species and it is long lived. The second site, Site S, located less than 10 kilometers from Site R contains two of the fungal species utilized by B. cornutus as hosts. Again, G. applanatum is present at Site S, but so is Ganoderma tsugae, a species that produces annual fruiting bodies and is constrained to

growing in central Pennsylvania only on eastern hemlock.

Although its fruiting bodies do not stay around as long as those of G. applanatum,

G. tsugae appears to produce a new mass of fruiting body at the start of every growing

season, while G. applanatum only puts on a new ring of reproductive material each year.

This scenario suggests a potential tradeoff between these two resources: G. applanatum

providing a more predictable location for the beetles to live within and G. tsugae

providing (at least in the first half of the growing season) a better food source, but one

which is ephemeral when compared to the longer-lived G. applanatum.

72 Within both of the sites we located all potential habitat patches for B. cornutus and geo-referenced their locations. In this study, we defined a habitat patch on which a local population of B. cornutus could exist as all fruiting bodies that were connected to one another by bark. Generally, a habitat patch was a single log or stump, but sometimes a patch consisted of two logs that had fallen across one another. The motivation for this definition of a patch is that the beetles have been found to move readily along bark between fruiting bodies. A similar protocol for defining habitat patches has been used by other researchers working with B. cornutus in the past (Whitlock 1992).

Capture Mark Recapture Methods

Once all local habitat patches were identified for both the single host and two host sites, we began our survey for beetles. We visited each local population within sites R and S every other week from the middle of May to the beginning of October. We carried out this procedure for the study years, 2003 and 2004. On each survey date, we visited all of the habitat patches within a site. We counted all observable beetles on the fruiting bodies of the fungi or on the bark of the patch between fruiting bodies. Any beetles within the fruiting bodies could not be counted as they could not be observed. These data, therefore, represent minimum population sizes of adult forked fungus beetles. Each beetle was also given its own unique marking using Testors© enamel paint. This information was used in other parts of the research to estimate parameters such as survivorship and recruitment. The sex of the beetle, body length (mm) and horn size category (small or large) were also recorded.

73

Estimating Minimum Population Sizes and Data for Testing for Synchrony

As mentioned above, we measured minimum population size for each of the local

populations on each observation event through the season. This measurement was used

because of the difficult nature of observing all adults present at one time period and any

of the larvae present as they are confined within the fruiting bodies of the fungal host. In this study minimum population size is defined as all observable adult B. cornutus on the fruiting bodies or bark of the tree on an observation date. Minimum population size was then used for calculating the change in each of the local populations over time. Because of the high variability in local population size that is inherent in a forked fungus beetle metapopulation, we did not use abundance directly to estimate synchrony between local populations. Instead we used the change in abundance between two observation periods.

For example: Obs2 – Obs1 = Chng1. We then used the calculated changes in abundance

over time for each of the local populations for our synchrony estimates.

Mean regional synchrony estimates and spatial synchrony estimates were calculated using a nonparametric spatial covariance function (Bjørnstad & Falck 2001) available as an extra library for R (R Development Core Team 2004). The zero lines in the spatial covariance plots represented in Figure 1 are the lines at which there is no synchrony. Within the 0 to positive 1 range there is some degree of synchrony, with 1 meaning total synchrony. The 0 to negative 1 range shows where the populations are asynchronous. Negative 1 indicates total asynchrony between populations. 95%

Confidence intervals were calculated for each of the estimates using bootstrap methods.

74

RESULTS

Do Local Populations within the Study Sites Exhibit Synchrony?

Both site R, the single host species site, and site S, the two host species site exhibited significant regional synchrony (Table 1) as both the estimate and confidence intervals were above the 0 synchrony line. Site R had an estimated mean regional synchrony of 0.244 (95% CI 0.069, 0.434) for year 1 and 0.143 (95% CI 0.011, 0.306) for year 2. The regional mean synchrony for site S was higher in both years than in site R.

In year 1, mean synchrony for site S was 0.379 (0.305, 0.461) and in year 2 0.244 (0.097,

0.363). We compared the estimated mean synchrony values for populations of B. cornutus as a function of the two different host species in site S (Table 1). We expected to see that the synchrony of populations inhabiting the perennial patches, G applanatum, would be similar to the mean synchrony of site R, which includes only habitat patches of

G. applanatum. The estimated synchrony values were not similar however. In both years the estimated mean synchrony was higher on populations inhabiting perennial host patches in the two host site, site S, than in the single host site, site R. Mean synchrony on the annual host patches in site S was also higher than mean synchrony in site R. From these results we find that having more than one host species increases the mean regional synchrony of an area over having only one host species present.

Does Synchrony Decrease Within a Site as a Function of Distance?

When synchrony is mediated by the dispersal of individuals between local populations, spatial correlograms tend to depict this signature as a decrease in synchrony as a function of distance between local populations Bjørnstad et al 1999). This is due

75 primarily to the limited ability of many organisms to disperse great distances and the

general nature of most individuals (though not all) to disperse to their nearest acceptable

neighboring population. Using spline correlograms we analyzed the synchrony data for

both sites and both years. We found that in general, synchrony did not decrease as a

function of distance (Fig 1a-g). For only the single host species site, R, in the first year,

our estimate of synchrony as a function of distance appears to decrease with distance and

even touches zero at the furthermost distance of the study site. The confidence intervals

around our synchrony estimates, especially at large distances, are large. This suggests

over the span of this study site, synchrony between populations does not significantly

decrease as a function of distance.

At the two host species site, site S, synchrony does not appear to change as a

function of distance between local populations in the first year (Fig 1c). The pattern

produced using spline correlogram for the second year of site S data (Fig 1d) shows a

decrease in synchrony at a lag distance of approximately 150 meters, followed by an increase in synchrony at greater distances. This pattern is typical when local populations

are highly synchronized, perhaps by a process such as dispersal, but whether this is

occurring here is difficult to say as the 95% confidence interval overlaps with the zero

synchrony line.

We divided the local populations of site S based on whether the local population

was inhabiting the perennial host species, G. applanatum, or the annual host species, G.

tsugae. The spline correlograms were then calculated for both of the two host categories

76 (Fig 1e-g). For those populations inhabiting the perennial host, synchrony appeared to

remain the same regardless of distance in the first year of data. In the second year,

synchrony was high for populations close together, but decreased at a separation of

approximately 150 meters and then increased again at further distances. This result mirrors the overall pattern exhibited by site S in year 2 when all local populations were

included in the analysis (Fig, 1d). There were not enough local populations inhabiting

annual host patches in the first year of the study for the calculation of the spline

corellogram. In the second year there were enough populations on patches of the annual

host. In year 2 we find no change in synchrony among the populations inhabiting the

annual host patches as a function of distance (Fig 1g).

From these results we hypothesize that synchrony within these sites occurs not

only because of dispersal between populations, but also as a function of the environment

experienced by the local populations. The seasonality of the hosts and the time period of

the study could be very influential on the local dynamics of these populations when

studied over the small time scale of two single growing seasons. The signature of the

mechanism synchronizing local populations could be clearer if the study was over a

longer period of time, or if experimental manipulations were made to exclude all but one

of the possible synchronizing mechanisms and then test for synchrony among the local

populations.

77 Are Populations from the Two Study Sites Synchronous?

The two study sites, site R and site S are located approximately 15 kilometers

away from one another. As forked fungus beetles are limited in their ability to disperse,

this distance is great enough to prevent any possibility of individuals dispersing from one study site to the other. This distance is, however, not great enough to separate regional

influences of weather and temperature. Increase in temperature in one of the sites is

mirrored by an increase in temperature at the other site. This is not to suggest that the two sites have the same temperatures over time, but rather that fluctuation in temperature at one of the sites is mirrored by a fluctuation in temperature in the same direction at the other site.

In conjunction with the ideas underpinning the Moran Effect, we hypothesized that because the two sites shared similar environmental fluctuations that the two sites should also exhibit synchronous population dynamics as well. We estimated the mean

between site synchrony for site R and S (Fig 2). For year 1 and year 2 respectively, the mean synchrony between sites were 0.085 (95%CI -0.031, 0.216) and 0.019 (95%CI

-0.075, 0.105). In both cases synchrony hovered near zero and the 95% confidence interval calculated with bootstrap techniques suggests that these values are not different from zero. Only in the first year (Fig 2a) did one of the sites, site S, differ significantly from the between sites synchrony estimate. From these results we conclude that density independent environmental factors shared between these sites do not alone result in the regional synchrony exhibited by both of the two sites when data were analyzed separately.

78

Do larger populations exhibit more synchrony than smaller ones?

For site R (Fig 3a-b), the site with only the perennial host species, G applanatum, population sizes binned in the following manner: bin 1: 1-15 individuals, bin 2: 16-26 individuals, bin 3: 27-52 individuals, bin 4: 53-118 individuals. Mean synchrony for each of the population bins was estimated and 95% confidence intervals were generated using bootstrap methods. In the first year of the study (fig 3a) we found no relationship between population size and mean synchrony for the single host site, site R. For year two, mean synchrony was lower for the larger populations than for the smaller populations.

We expected to see mean synchrony increase as a function of population size, as often larger populations are less impacted by demographic stochasticity. For the two host site, site S, mean synchrony does not appear to differ as a function of population size as all confidence intervals have large overlap (Fig 3c). Even though there is great overlap of confidence intervals, meaning that there is no significant difference between the estimates across population sizes, there is a trend suggesting that mean synchrony does increase as population size increases. This trend is much stronger in the second year (Fig 3d). In year 2 for site S mean synchrony increases as population size increases. Because of the small sample size that would result for each bin, we did not break down the results for site S by host species to see if this trend was the result of one host species or if it was common for both host species in site S.

79 DISCUSSION

We investigated the role of landscape structure (one host or two hosts present)

and host species on the synchrony of local population dynamics. Because of the

seasonality of the annual host, G. tsugae, we expected regional synchrony to be higher in

the two host species metapopulation than in the single host metapopulation. In general,

the mean regional synchrony was greater in the two host site for both years than in the

single host site. The single host site did, however, still display regional synchrony that

did not decrease as a function of distance between local populations. We cannot be sure

whether this is a real effect of the different landscapes because we have no replications.

It is difficult to replicate this study because of its large spatial scale and because the

likelihood of finding multiple large areas that have the same number of the different host

patches present and at the same distances is close to impossible. In order to have

replications we would need to have an extremely large experimental setup.

Regional synchrony within the two host site did not decrease with distance in the first

year of the study, but appeared to decrease with distance in the second year, although the

confidence intervals are large and suggest no significant difference in synchrony across

the spatial scale of the study site.

Synchrony between local populations is often explained in the literature as a result

of dispersal between populations, the effects of natural enemies, or the Moran effect

(Ranta et al 1995). Often the pattern of synchrony observed from spatial correlograms can help the researcher determine if the pattern is a function of dispersal or a more regional effect, such as the Moran effect (Bjørnstad et al, 1999). From the data we

80 collected for both sites, synchrony tended to remain the same across distance classes between patches. Often one would consider such results to suggest that dispersal is not the key factor synchronizing the dynamics of local populations. A lack of decline in synchrony over space suggests environmental factors are of more importance in the maintenance of synchrony than dispersal. One problem with such a conclusion is that the spatial scale over which synchrony is measured can often impact whether or not synchrony is seen to decrease or remain the same with larger distance classes.

For our study sites, it is possible that the general lack of a decline in synchrony with distance is a result of the small sizes of the study sites. For the single host site, site

R, the largest distance between two patches was 250 meters, while for site S, the two host site, the largest distance class was 550 meters. From the dispersal data gathered in this study (see Chapter 4) we found that some individuals were capable of dispersing across these largest distance classes. Most individuals dispersed a much shorted distance, but these few individuals who did exhibit longer distance dispersal could be sufficient for synchronizing the dynamics between local populations separated by the greatest distance within a site.

In order to better understand the role of environmental correlations on the regional synchrony of local populations of forked fungus beetles we estimated the mean regional synchrony when local populations from both of the sites are analyzed together. In this case, mean between site synchrony for both years approached zero. This result suggests that some aspects of a shared environment, including fluctuations in temperature and

81 weather patterns did not act to synchronize the dynamics across the two sites (or were not the only factors working to synchronize populations).

If we wish to better understand the mechanism or suite of mechanisms at work in synchronizing the dynamics of local populations we need to either conduct the study at a larger spatial scale and/ or conduct experiments in which the influence of only one potential mechanism is allowed and all others are controlled. Such a study is especially difficult to do when working with real populations in a field setting. Effects such as weather and temperature are hard to control for in the field. In order to efficiently determine the mechanism responsible for population synchrony, one must either work with populations in a microcosm/ laboratory setting (Gonzalez and Holt, 2002) or collect data from organisms inhabiting islands separated by a great enough distance to rule out the effects of dispersal on population synchrony (Grenfell, 1998).

Real metapopulations are often constructed of local populations that vary greatly in size (Gonzalez & Holt 2002, Reed and Hobbs 2004). Studies suggest that size of populations can have a great influence on the synchrony of populations, with larger populations often having a higher level of synchrony than smaller populations (Grenfell et al 2001). We found that in the single host site, there was no effect of population size on the level of synchrony expressed. This held true for both years of data. For the two host site, site S, we found a general trend in both years for synchrony to increase as population size increased. Because of the small sample size that would result, we were

82 unable to analyze how synchrony across population size classes differed on the two host patch types in site S.

In this study we found that the two different landscapes, one with a single host species and one with two host species, resulted in different levels of regional synchrony

in forked fungus beetle metapopulations. Both sites exhibited synchrony within each site,

but there was no synchrony between sites. Host species influenced mean regional

synchrony, but not as we had originally expected. We expected the mean synchrony of

the perennial host patches in site S to be similar to the mean regional synchrony of site R,

since it was composed entirely of the same species of host, G. applanatum. This was not the case. Perennial patches in site S had a higher mean synchrony than perennial patches in the single host site. The higher mean synchrony could be the result of interactions

with the local populations on nearby hosts through the act of dispersal. Dispersing

individuals from annual patches to perennial patches could act to increase synchrony. In

general the results of this study indicate that metapopulations of the same species

experience very different landscapes and the inherent differences in spatial structure

could result in very different levels of synchrony between metapopulations.

83

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84 Liles, M. P. 1956. A study of the life history of the forked fungus beetle, Bolitotherus cornutus (Panzer) (Coleoptera: Tenebrionidae). Ohio Journal of Science. 56: 329- 37.

Matter, S. F. 2001, Synchrony, extinction, and dynamics of spatially segregated, heterogeneous populations. Ecological Modelling. 141(1-3): 217-226.

R Development Core Team. 2004. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3- 900051-00-3, URL http://www.R-project.org.

Ranta, E., V. Kaitala, & H. Linden. 1995. Synchrony in population dynamics. Proceedings of the Royal Society of London: Biological Sciences. 262 (1364): 113-118.

Reed, D. H. 2004. Extinction risk in fragmented habitats. Animal Conservation 7: 181- 191.

Reed, D. H., & G. R. Hobbs. 2004. The relationship between population size and temporal variability in population size. Animal Conservation. 7: 1-8.

Singh, B. K., J. S. Rao, R. Ramaswamy, & S. Sinha. 2004. The role of heterogeneity on the spatiotemploral dynamics of host-parasite metapopulation. Ecological Modelling 180(2-3): 435-443.

van Nouhuys, S. & G. C. Lei. 2004. Parasitoid-host metapopulation dynamics: the causes and consequences of phenological asynchrony. J. of Animal Ecology 73(3): 526- 535.

Whitlock, M. C. 1992. Nonequilibrium population structure in forked fungus beetles: extinction, colonization, and the genetic variance among populations. American Naturalist. 139: 952-970.

85 4-1a: Proportion Large Males in Single Host Site R 4-1b: Proportion Small Males in Single Host Site R as a Function of Number of Females as a Function of Number of Females

4-1c: Proportion of Large Male in Two Host Site S 4-1d: Proportion of Small Males in Two Host Site S as a Function of Number of Females as a Function of Number of Females

4-1e: Proportion of Large Males on Perennial Patches 4-1f: Proportion of Small Males on Perennial Patches Site S as a Function of Number of Females Site S as a Function of Number of Females

4-1g: Proportion of Large Males on Annual Patches 4-1h: Proportion of Small Males on Annual Patches Site S as a Function of Number of Females Site S as a Function of Number of Females

Figure 4-1a-h: Boxplot of the proportion of large horned males and small horned males as a function of the number of females present in the local population. Fig 4-1a-b represent the single host species site. site R. Fig 4-1c-d represent all of the local populations in the two host species site, site S. Fig 4-1e-f represent local populations on only the perennial host species, Ganoderma applanatum. Fig 4-1g-h represent local populations on only the annual host species, Ganoderma tsugae. 86 4-2a: Local Population Sizes in the One Host Species Site, Site R

140

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4-2b: Local Population Sizes in the Two Host Species Site, Site S

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Figure 4-2a-b: Histogram of range of local population sizes observed during th two year study period. Site R is the single host species site and is represented in Fig 4-2a. SIte S is the two host species site and is represented in Fig 4-2b. The dark gray lines are local populations observed on patches of the perennial host species, Ganoderma applanatum. The light gray lines are local populations observed on patches of the annual host species, Ganoderma tsugae.

87 Fig 4-3a: Single Host Site: Large Male-Julian Date Fig 4-3b: Single Host Site: Small Male- Julian Date

Julian Date Julian Date

Fig 4-3c: Two Host Site All Patches: Fig 4-3d: Two Host Site All Patches: Large Male- Julian Date Small Male- Julian Date

Julian Date Julian Date Fig 4-3e: Two Host Site Annual Patches Only: Fig 4-3f: Two Host Site Annual Patches Only Large Male Small Male

Julian Date Julian Date Fig 4-3g: Two Host Site Perennial Patches Only: Fig 4-3h: Two Host Site Perennial Patches Only; Large Male Small Male

Julian Date Julian Date

Figure 4-3a-h: Boxplots of proportion of large horned males and small horned males as a function of Julian Date. Fig 4-3a-b represent the single host species site, site R. Fig 4-3c-d represent all of the local populations on the two host species site, site S. Fig 4-3e-f represent local populations on patches of only the perennial host species, Ganoderma applanatum. Fig 4-3g-h represent local populations on patches of only the annual host species, Ganoderma tsugae, 88 Fig 4-4a: Recruitment Rates One Host Site: Site R Fig 4-4b: Normalized Recruitment Rates One Host SIte: Site R 1 70

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Fig 4-4c: Recruitment Rates Two Host Site: Fig 4-4d: Normalized Recruitment Rates Two Host Site:

Perennial Patches Only, Site S 60 1 Perennial Patches Only, SIte S

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1 Annual Patches Only, Site S Annual Patches Only, Site S 40

35 0.8 30

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Fig 4-4a-f: Plots of recruitment rates over time estimated using Program Mark (Figs 4-4a, 4-4c, 4-4e) as well as plots of recruitment rates normalized by the representation of the three sex categories over time. Figs 4-4a & 4-4b represent the single host species site, site R. Female Figs 4-4c & 4-4d represent recruitment on the perrenial host patches, Large Horned Male Ganoderma applanatum, in the two host species site, site S. Figs 4-4e & 4-4f represent recruitment on the annual host patches, Small Horned Male Ganoderma tsugae, in the two host species site, site S.

89 Year 1 Year 2 Site R and Site S 0.085 (-0.031, 0.216) 0.019 (-0.075, 0.105)

Site R 0.244 (0.069, 0.434) 0.143 (0.011, 0.306)

Site S 0.379 (0.305, 0.461) 0.244 (0.097, 0.363)

Site S Annual Patches 0.347 (0.163, 0.525) 0.246 (0.062, 0.306)

Site S Perennial Patches 0.435 (0.344, 0.533) 0.229 (0.038, 0.305)

Table 4-1: Regional Synchrony Estimates (with 95% confidence intervals in parentheses). First row represents mean synchrony between both Sites R and S. Rows 2 & 3 are mean regional synchrony for Site R alone and Site S alone respectively.

90 Chapter 5: Dispersal and Patch Connectivity as a Function of Habitat Heterogeneity and Habitat Patch Size in Metapopulations

ABSTRACT

In the context of a metapopulation, the act of dispersal joins spatially separate local populations. The degree to which local populations within a metapopulation are coupled via dispersal can have both positive and negative impacts on the longevity of both local populations and the metapopulation as a whole. Dispersal can vary both spatially and temporally and can greatly impact dynamics and genetics at both the local population and metapopulation level. The landscape that populations inhabit has the ability to greatly influence the dispersal propensity between local populations and hence the degree to which local populations interact with one another. For this reason, landscape composition can select for different dispersal strategies of individuals inhabiting different metapopulations. In this study we examine the role of habitat heterogeneity on the dispersal strategies of forked fungus beetles, Bolitotherus cornutus.

We also examine the effect of habitat patch size on the degree of connectivity within the metapopulation.

In general, we found that there are differences between our two study sites, the single host species study site and the two host species study site, in the proportion of the total individuals that disperse, and who among the classes of female, large horned male, and small horned male are dispersing. In the two-host species metapopulation, we also found that the species of host on which one originates can greatly impact the host species to which an individual disperses. In both the single host species metapopulation and the

91 two host species metapopulation we found that the size of a habitat patch was positively correlated with number of immigrants and emigrants. Connectivity of habitat patches was at is highest between large habitat patches, with most of the large habitat patches transferring immigrants and emigrants between one another. The results of this study suggest that both host species composition in the landscape and the range of sizes in local habitat patches can greatly impact the dynamics and persistence of populations (both local populations and metapopulations), as well as drive the evolution of host use and dispersal strategies.

92 INTRODUCTION

Dispersal is a key life history trait for the majority of species, having a great influence on both the population dynamics and evolution of a population or set of populations. The act of dispersing is generally defined as a unidirectional movement away from a place of origin to another location (Clobert et al 2001). The causality of dispersal, whether the focus is on the proximate or the ultimate cause, varies between species (Greenwood & Harvey 1982, Holekamp 1986, Alonso et al 1998, Ferreras et al

2004), and as more recent studies suggest, between populations of the same species and individuals within a population as well (Lawrence 1987, Johnson & Gaines 1990,

McPeek & Holt 1992, Denno et al 1996).

Within the context of a metapopulation, dispersal is the mechanism by which spatially separated local populations are connected. By definition, dispersal occurs in metapopulations, but is rare enough that local populations still exist as separate entities

(Hanski 1989). The degree to which local populations are connected to one another via dispersal can have a large impact on the local and regional dynamics of the population(s) as well as on the degree of gene flow among populations (Wright 1952). Theoretical studies have shown that in many scenarios only a few individual dispersers are needed within the metapopulation in order for regional persistence to occur (Fahrig & Merriam

1985, Stacey & Taper 1992). Other studies suggest that too much movement of individuals between local populations can have detrimental effects on the persistence time of a metapopulation through its ability to synchronize local population dynamics

(Chesson 1981).

93

The study of adaptive dispersal has been an important area of theoretical research in ecology and evolution (Slatkin 1985, Johnson & Gaines 1990, Levin et al 2003), and a metapopulation scenario has proven to be an interesting avenue for studying this as well as the evolution of other life history traits (Gandon & Michalakis 1999, Dytham 2003,

Parvinen et al 2003, Yukilevich 2005). The inherent spatial structure of a metapopulation coupled with population turnover and the tradeoff between too little and too much dispersal in such a system make it an ideal scenario through which selection can act (Whitlock 1992). To date the majority of the literature on dispersal evolution in a metapopulation has been theoretical in nature. The next step in this area of research is to test the results from such theoretical models empirically. Such outputs to be tested include: (a) dispersal rates vary as a function of (i) distance between patches, (ii) patch quality, and (iii) habitat heterogeneity (Van Valen 1971, Olivieri et al 1990, McPeek &

Holt 1992) (b) the degree of dispersal is impacted by variability in population sizes across the metapopulation (Levin et al 1984).

Our study expands on the literature of adaptive dispersal by testing empirically some of the predictions of the theoretical models. In particular, we looked at the role of habitat heterogeneity in the form of the number of host species present as habitat patches in the landscape on the dispersal strategies of forked fungus beetles, Bolitotherus cornutus. This empirical study was conducted using capture-mark-recapture of individually marked beetles over a two year time span. In particular we focused on the role of landscape heterogeneity on dispersal propensity and distance among individual

94 beetles, as well as between sex and male size category (large horned males and small horned males). We also examined how dispersal decisions differ as a function of the host species from which a beetle originates, and the size of the population from which a disperser originates.

METHODS

We compared the role of habitat heterogeneity in the form of the number of host species in the landscape present by first identifying two locations, one with habitat patches of only one host species, Ganoderma applanatum, and one site in which two host species were represented as habitat patches in the landscape, G. applanatum and

Ganoderma tsugae. Both of these study locations are within the boundaries of Rothrock

State Forest, Centre County, Pennsylvania. The two host species are polypore shelf fungi

that infest decaying or dead trees. The two species differ in their range of host species as

well as in the longevity of their fruiting bodies, which are the resource utilized by all life

stages of forked fungus beetles. These qualities, both the spatial arrangement of the

fungal hosts as a function of the geographic distribution of their tree hosts, and the

difference in temporal permanence in the landscape can have a great impact on many

traits associated with dispersal.

After locating the two study sites and demarcating a boundary around them, we

located all habitat patches within the boundary, geo-referenced the patches, and recorded

information about the host species of the patch, the number of fruiting bodies within the

patch and their sizes. We individually marked all beetles found on these habitat patches

95 throughout the two year study period and checked for the locations of the beetles every

two weeks. For each beetle, we recorded relevant biological information such as body length (mm), sex, and horn length category (small or large) for males. We used the capture-mark-recapture data from the two years in conjunction with the geo-referenced locations of the habitat patches to study the dispersal patterns of B. cornutus in the one host species landscape, site R, and the two host landscape, site S.

Does Dispersal Differ Between the One Host Species and Two Host Species Landscape?

In order to compare some of the general difference between dispersal in the one host species landscape, site R, and the two host species landscape, site S, we generated a summary table made up of information about the total number of dispersers in the two different study sites, the total number of female dispersers, large horned male dispersers, and small horned male dispersers. The values reported represent all of the observed dispersers, meaning the sighting of an individual that was originally discovered on one habitat patch later to be on a different habitat patch.

Is there a Relationship between the Host Species on which a Beetle Originates and the

Host Species a Beetle Disperses to?

To determine whether beetles disperse to a new habitat patch based on host choice or to nearest patch neighbor regardless of host species, we compared the observed dispersal patterns of the beetles from the field sites with the null pattern produced under the assumption that beetles disperse from their population of origin to their nearest neighbor local population. The data utilized in this part of the paper were those collected

96 from only the two host species site, site S. We used program R (R Development Core

Team 2004) to develop the null outcome for nearest neighbor dispersal based on the spatial coordinates of the habitat patches in site S. We compared the observed results with those expected from the null using chi square statistics.

How Far do Beetles Disperse in the Landscape?

We compared the distances dispersed by beetles in the one host species landscape and the two host species landscape as well as between sex categories (female, large horned male, and small horned male). Distance matrices were calculated using program

R (R Development Core Team 2004) for both of the study sites. Because the data are not normally distributed, we calculated the median distance dispersed for each of the categories as well as dispersal distance range (minimum and maximum).

Is there a Relationship between the size of a Habitat Patch and Immigration, Emigration, and Connectivity?

We analyzed emigration and immigration as a function of patch size using linear regression (Table 4, Figure 4). In this case, patch size is the number of fruiting bodies within a habitat patch. We also tested for correlations between the number of populations contributing dispersers to a single population and the number of populations to which a single population contributes.

97 RESULTS

Does Dispersal Differ Between the One Host Species and Two Host Species Landscape?

Within the single host species site, site R, 772 adult beetles were marked during

the two year capture-mark-recapture study. Of these 772 beetles, 158 of them were

observed to have dispersed from their original local population to another local

population within the site. The results indicate that in the single host species landscape,

20% of the beetles marked dispersed once during this two year period. In the two host

species site, site S, we marked 1193 adult beetles and followed them throughout the two

year study. Of these 1193 marked beetles, 176 were observed to have moved from their

original local population to another local population within the landscape. These 176

dispersers represent 15% of the total number of beetles marked within the two host

species landscape (Table 1). Using contingency table analysis, the difference in the

proportion of individuals dispersing in the single host species vs. the two host species

landscape is significantly different (chi sq. 10.845, d.f. =1, pvalue <0.001).

Table 1 summarizes the representation of adult females, large horned males and

small horned males represented in the total number of beetles observed in the two

landscapes as well as the total number of dispersers observed in the two landscapes. In both the single host species and two host species sites the sex ratio of B. cornutus is

skewed, with a female bias. We found that in the single host species landscape, 57% of

all beetles marked were female and 43% were males. Of the males marked, 27% were

large horned males and 16% were small horned males. In the two host species site, site

S, 63% of all beetles marked were females and 37% of all beetles marked were males.

98 Of the males marked, 21% were large horned males and 16% were small horned males.

In both of the landscapes more large horned males than small horned males were

observed and marked, however in the single host species landscape the difference

between the proportion of large horned and small horned males is greater than in the two

host species landscape.

There are two ways to summarize the dispersal data gathered from the two year

study. The first is to look at whether the proportions of the three sex categories in the

dispersal data are representative of the proportions of the three sex categories observed in the total number of beetles marked. In both of the sites the representation of all three sex categories in the total number of beetles marked and the total number of beetles observed to disperse is similar. A second way to summarize the dispersal data among the sex categories is to ask what proportion of individuals within each of the categories moved and whether this differs between the single host species and two host species landscape.

We found that in the single host landscape 19% of all females marked dispersed and in the two host species landscape 16% of all females dispersed. Within the large horned male category, 25% of all large horned males marked in the single host species site dispersed while only 13% of all large horned males marked in the two host species site dispersed. Within the small horned male category, 18% of all small horned males within the single host species site dispersed while only 12% of all small horned males marked in the two host species site dispersed

99 Is there a Relationship between the Host Species on which a Beetle Originates and the

Host Species to which a Beetle Disperses?

Because this question is only relevant for the study site containing more than one host species, we concentrated entirely on the data gathered from the two host species landscape to address this question. As mentioned in the Materials and Methods section, we assumed the null pattern of dispersal would be individuals dispersing to their nearest neighboring population regardless of the host species inhabited by this nearest neighbor.

We compared the observed dispersal data to the generated null dispersal pattern. The pattern of dispersal observed by beetles that originated on habitat patches of the perennial host species, G. applanatum did not significantly differ from the null pattern (Figure 1a,

Table 2). Interestingly, the pattern of dispersal observed by beetles that originated on habitat patches of the annual host species, G. tsugae, did differ significantly from the null pattern of dispersal to nearest neighbors (p <0.001) (Table 2).

Given the distances between the patches in the landscape, we expected to see a greater proportion of individuals originating from annual habitat patches dispersing to perennial habitat patches than annual habitat patches. The observed pattern was reversed, signifying active host choice. There were more individuals originating on annual habitat patches dispersing to annual habitat patches even though they were often not the nearest neighbor (Figure 1b). This pattern held when we broke the data down by sex category

(Fig 2a-h). Regardless of sex category, the dispersal pattern of beetles originating on patches of the perennial host species did not differ from the null of pattern of nearest neighbor dispersal (Table 2). All sex categories differed significantly from the null when

100 the individuals originated on a habitat patch of the annual species (Table 2). Again, there

were more individuals moving to habitat patches of the annual host species than expected

from the null distribution generated under the assumption of nearest neighbor dispersal.

How Far do Beetles Disperse in the Landscape?

For the single host species site, site R, we found that most beetles that dispersed

moved between 1.4 and 50 meters. The maximum dispersal distance for the site was

211.40 meters. For the two host species site, site S, the majority of beetles that dispersed

moved between 5 and 100 meters from original habitat patch. The maximum distance

dispersal was observed in the two host species site was 550.81 meters (Table 3). On

three occasions in the summer we scouted habitat patches outside of the boundaries of our two study sites in order to see if we detected longer distance dispersal events. On the three occasions, we found no beetles marked within the study sites on habitat patches outside of our study site. This does not definitively rule out longer distance dispersal than that observed within our study sites, but rather suggests that in order to detect any longer distance dispersers a more thorough sampling regime is needed. The primary reason for this being that the probability of detection is lower as one searches at a larger radius from the center of the sampling site.

101 The median distance moved by females in the single host species site, site R was

1.41 m., while in the two host species site, site S, it was 32.8 m (Table 3). The minimum and maximum distances moved by females in the single host site were 1.41 and 170.66 m respectively. In the two host species site the minimum and maximum distances dispersed by females was greater: 4.0 m and 550.81 m respectively. This difference in the median dispersal distance and range of dispersal distances for females in the two different landscapes suggests that some aspect of the two host species landscape leads to females dispersing further distances than in the single host landscape. This difference may not be driven by the host species composition of the sites, but could instead be a result of the distances between suitable habitat patches in these two sites (independent of host species composition of site).

Large horned males from the single host species site, site R, had a median dispersal distance of 54.33 m, while large horned males in the two host species site, site

S, had a median dispersal distance of 27.31 m. The range of dispersal distances for large horned males was 1.41 to 196.49 m in the single host species site and 5.10 to 550.81 m in the two host species site. For the small horned males, the median dispersal distance in the single host species site, site R, was 1.41 m and in the two host species site, site S, was

47.42 m. For both females and small horned males, the median dispersal distance was greater in the two host species site, while for the large horned males median dispersal distance was greater in the single host species site.

102 Is there a Relationship between the size of a Habitat Patch and Immigration, Emigration,

and Connectivity?

We found a significant (Table 4) positive relationship between the size of a habitat patch, as measured by the number of fruiting bodies in a habitat patch, and the

number of emigrants (number of individuals leaving a patch) for both of the study sites

(Figs 4a & 4c). Immigration (moving to a new habitat patch) was also positively

correlated (Table 4) with size of habitat patch for both of the sites (Figs. 3b & 3d). The

correlation was greater for both immigration and emigration in the two host species site,

site S (Table 4).

In order to get a better picture of the degree of interaction between local

populations we looked at the number of different local populations a single local

population receives immigrants from and the number of local populations a single population sends emigrants to. We found that the median number of populations from which a single population receives immigrants is 1 for the single host species site and 2 for the two host species site. The range (minimum and maximum values) are from 0 to

10 local populations for the single host site, site R, and between 0 and 7 for the two host site, site S (Table 5). This large range suggests that there are some local populations that are more connected to one another via dispersers than other local populations. The median number of local populations emigrants from a single local population join are 2 for the single host site, site R, and 1.5 for the two host site, site S (Table 5). Again, the range is large. For site R, the range of values is 0 to 8 local populations and for site S the range is 0 to 11 populations. We find that some local populations contribute emigrants to

103 many other local populations within the metapopulation while others do not contribute

emigrants to many local populations.

From these results we find that the interactions among local populations within

the metapopulation are not equal among all habitat patches. We therefore examined whether the number of populations a single population contributed to and the number of populations that contributed immigrants to that same population were correlated. We found a significant positive relationship between these two factors (Table 6, Fig 5a-d) for three of the four data plots. The only data set without a significant relationship was for only the annual habitat patches in the single host site, site S, although it still had a p value of 0.065. There are few data points for this plot because the smaller number of annual

habitat patches within the two host site as compared to the number of perennial habitat

patches. In general, these results suggest dispersers tend to be cosmopolitan, with

individuals dispersing from a large local population tending to relocate to another local

population of large size.

104 DISCUSSION

In this study we focused on the role of habitat heterogeneity on dispersal

strategies of the forked fungus beetle, Bolitotherus cornutus. Previous studies in the

literature have indicated that spatial and temporal variability in the landscape can select

for different dispersal strategies, These changes may be reflected in populations of the

same species separated spatially or can occur as a trajectory within the same population

over time, or most probably some combination of the two. The majority of these studies

have been theoretical in nature, with few empirical studies to test the results of such

theoretical models. In this empirical study we looked for differences in the dispersal

strategies of beetle metapopulations spatially separated and exposed to differences in host

species availability, as well as differences in dispersal strategies of beetle populations within the same population but reared on different host species. The latter of these two also deal with spatial segregation, but from the results one can also speculate how within

the whole metapopulation dispersal strategies may change over time as a function of host

choice. Our findings suggest that habitat patch heterogeneity, in the form of host species

and patch size, can have an impact on the dispersal patterns observed across

metapopulations and that within a metapopulation itself patch heterogeneity can greatly

influence the dispersal patterns observed. The positive correlation between patch size

and connectivity via immigrants and emigrants suggests that conspecific attraction can be

an important factor driving metapopulation dynamics.

105 Between the single host species metapopulation and the two host species

metapopulation we found in general that the proportion of beetles within the

metapopulation that dispersed between local populations differed significantly. In the

single host species landscape, 20% of the total number of beetles marked dispersed, while

in the two host species landscape only 15% of the total number of beetles marked in the study dispersed. Whether this difference is due to the makeup of the landscape in terms of host species, or some other underlying quality of the landscape remains unknown. The difference is large enough to suggest that it could be of biological importance and deserves further studies. Even though there are more dispersers in the single host species landscape, it could very well be that in such a landscape there are always a small number of dispersers throughout the season, whereas in the two host species landscape where the seasonality of the annual host species is very strong, most of the dispersal happens at the same time and is not spread out through the season. It would be very interesting to look at the timing of dispersal in both systems. In such a case, whether dispersers are moving throughout the season in low numbers or whether they move in a similar time window could have very different impacts on local population dynamics as well as on the dynamics at the level of the metapopulation.

In both of the metapopulations we found that the size of the habitat patch, which is positively correlated with population size, has a great impact on the degree of connectivity individuals from that patch have with individuals from other patches in the form of immigrants and emigrants. The larger habitat patches not only sent out more emigrants than smaller sized habitat patches, but also received more immigrants than the

106 smaller habitat patches. We also found that habitat patches of large size received immigrants from a greater number of habitat patches than their smaller counterparts and that large habitat patches sent emigrants to a larger number of different habitat patches than the small habitat patches did. These results suggest that forked fungus beetle metapopulations do not necessarily fit into the concept of a source-sink metapopulation

(Pulliam 1988) in which large populations tend to send dispersers to small populations which otherwise would exhibit negative growth rates, but at the same time they do not fit the classic definition of a metapopulation defined by Levins (1969) in which all local populations have equal probability of receiving dispersers from one another regardless of location or patch characteristics. The pattern generated in this scenario is more cosmopolitan in that beetles from the “larger cities” tend to move to other large cities rather than the surrounding “small towns”. In general the larger patches interact more with one another through their dispersers than they do with small populations. These movement patterns could greatly impact the local dynamics of the beetle populations as well as gene flow between the populations.

Within the two host species metapopulation, we found that the host on which a beetle originates can greatly impact the decision of where that individual will disperse to.

When the beetle that dispersed originated on the host species producing perennial fruiting

bodies, Ganoderma applanatum, it almost always dispersed to its nearest neighbor

regardless of what host species the nearest neighbor was. Those beetles that dispersed

from a patch of the host species producing annual fruiting bodies, Ganoderma tsugae, did not show a similar pattern. They dispersed more often than expected from the null of

107 nearest neighbor dispersal to other habitat patches of G. tsugae. This pattern suggests that beetles originating on G. tsugae have formed some level of preference for G. tsugae over G. applanatum. Because G. tsugae produces annual fruiting bodies that decay through in the later part of the season and because forked fungus beetles are long lived insects, we expected to see more individuals showing a preference for the perennial host as it is around for a longer time in the landscape. Such a pattern was found in a study by

Denno and collaborators (1991) in which the persistence of certain habitats was found to influence the dispersal of planthoppers. It could very well be that the timing of dispersal from annual patches to annual patches and from annual patches to perennial patches differs. It may be that dispersing beetles from annual patches choose other annual patches over nearest neighbors early in the season when the quality of the host is still high, while in the later part of the season they disperse more often to perennial patches for a better overwintering location. This idea would be very interesting to test in the future.

Because this study was an observational one, the results are all correlative and act only to suggest possible relationships and mechanisms driving dispersal evolution / selection for different dispersal strategies. The next step for such a study is to conduct controlled experiments, such as common garden experiments, to better understand the role of habitat heterogeneity on dispersal evolution. This would require multiple replicates of single host species and two host species landscapes in which the number of patches, distance between patches, and spatial arrangement of the host species remains constant among replicates. One could also test for genetic differences in host choice

108 behavior as well. The two main goals of this study were to first identify an empirical system that could be conducive for testing hypotheses related to dispersal evolution in different environments and second conduct initial studies to see if there are in fact different dispersal patterns in the two environments. The results of the study suggest that there are in fact differences. The next step is to test whether these differences are of biological importance, what are the mechanisms leading to these differences, and what do these differences mean for metapopulations as a whole.

109

LITERATURE CITED

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Denno, R. F., G. K. Roderick, K. L. Olmstead, & H. G. Dobel. 1991. Denstiy-related migration in planthoppers (homoptera, delphacidae)- the role of habitat persistence. American Naturalist, 138. 1513-1541.

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110 Holekamp, K. E. 1986. Proximal causes of natal dispersal in fee living Belding’s ground squirrels (Spermophilus beldingi). Ecological Monographs. 56: 365-391.

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112 Fig 5-1a: Beetles moving from a Perennial Patch

100 expected observed 90 p=0.211 80

70

60

50

40

30

Number of beetles moving 20

10

0 1 P to P P to A P to P P to A

Fig 5-1b: Beetles moving from an Annual Patch

50 expected observed 45 p=0.0002 40

35

30

25

20

15

Number of beetles moving 10

5

0 1 A to A A to P A to A A to P

Figure 5-1a-1b: Dispersal patterns between the two host types. P represents the perrenial host, Ganoderma applanatum, and the A represents the annual host, Ganoderma tsugae. Expected dispersal patterns are based on calculations of nearest neighbors from real site landscape.

113 Fig 5-2a:females Females moving moving from from Perennial a Perennial Patch Fig 5-2b: Femalesfemales movingmoving from from annuals an Annual Patch

80 expected observed 25 expected observed

70 20 60

50 15 40

30 10

20 5 10

0 Number of beetles moving

Number of beetles moving 0 1 P to P P to A 1 P to P P to A A to A A to P A to A A to P

Fig 5-2c:males Males moving moving from fromPerennial a Perennial Patch Fig 5-2d: Malesmales moving moving from from annual an Annual Patch

30 expected observed 18 expected observed 16 25 14

20 12 10 15 8

10 6 4 5 2

0 Number of beetles moving 0 1 P to P P to A 1 P to P P to A Number of beetles moving A to A A to P A to A A to P

Fig 5-2e: Large Horned Males moving from Fig 5-2f: Large Horned Males moving from a Perenniallarge malesPatch moving from Perennial an Annuallarge malesPatch moving from annual

expected observed 12 expected observed 20 18 10 16

14 8 12 10 6 8 4 6 4 2 2

0 0 Number of beetles moving Number of beetles moving 1 1 P to P P to A P to P P to A A to A A to P A to A A to P

Fig 5-2g: Small Horned Males moving from Fig 5-2h: Small Horned Males moving from a Perennialsmall males Patch moving from perennial an Annualsmall Patch males moving from annual

12 expected observed 7 expected observed

6 10

5 8 4

6 3

4 2

2 1

0 0 Number of beetles moving Number of beetles moving 1 1 P to P P to A P to P P to A A to A A to P A to A A to P Figure 5-2a -h: Dispersal patterns of females (5-2a&b), males (5-2c&d), large horned males (2e&f), and small horned males (2g&h) between the two host types. P represents the perrenial host, Ganoderma applanatum, and the A represents the annual host, Ganoderma tsugae. Expected dispersal patterns are based on calculations of nearest neighbors from real site landscape. 114 Fig 5-3a: Site R All Individuals Fig 5-3b: Site S All Individuals Num. of Individuals Num. of Individuals

Distance (meters) Distance (meters)

Fig 5-3c: Site R Females Fig 5-3d: Site S Females Num. of Females Num. of Females

Distance (meters) Distance (meters)

Fig 5-3e: Site R Large Horned Males Fig 5-3f: Site S Large Horned Males Num. Lg Males Num. Lg Males

Distance (meters) Distance (meters)

Fig 5-3g: Site R Small Horned Males Fig 5-3h: Site S Small Horned Males Num. Sm Males Num. Sm Num. Sm Males Num. Sm

Distance (meters) Distance (meters) Figure 5-3a-h: Historgrams of dispersal distances. Site R represents the single host species site and site S represents the two host species site. 5-3a & 3b have all individuals that were observed to have dispersed within site R (5-3a) and site S (5-3b). Dispersal distributions of only females are depicted in 3c (single host species site) and 3d (two host species site). Dispersal distributions of only large horned males are depicted in 5-3e (single host species site) and 5-3f (two host species site). Dispersal distributions of only small horned males are depicted in 3g (single host species site) and 3h (two host species site).

115 Fig 5-4a: Emigration as a function of patch size in Fig 5-4b: Immigration as a function of patch size in single host species site R. single host species site R. Number of beetles emigrating Number of beetles immigrating

Number of Fruitning Bodies in Patch Number of Fruitning Bodies in Patch

Fig 5-4c: Emigration as a function of patch size in Fig 5-4d: Immigration as a function of patch size in two host species site S. two host species site S. Number of beetles emigrating Number of beetles immigrating

Number of Fruitning Bodies in Patch Number of Fruitning Bodies in Patch

Figure 5-4a-d: The effect of patch size (number of fruiting bodies in a patch) on immigration to (Figs5-4a&c) and emigration from (Figs 5-4b&d) patches. Figs 5-4a&b represent the single host species site, site R, and Figs 5-4c&d represent the two host species site, site S. Results of linear regressions performed on the data are reported in Table 5-2.

116 Fig 5-5a: Single Host Species Site, Site R Fig 5-5b: Single Host Species Site, Site S

p<0.0001 p<0.0001 Num donor populations Num donor populations

Num populations contributed to Num populations contributed to

Fig 5-5c: Site S Local Populations on Annual Patches Fig 5-5d: Site S Local Populations on Perennial Patches

p=0.065 p<0.0001 Num donor populations Num donor populations

Num populations contributed to Num populations contributed to

Figure 5-5a-d: Correlations between the number of patches an individual patch contributes dispersers to and the number of patches that an individual patch receives dispersers from. Fig 5-5a: all patches in the single host species site, site R. Fig. 5-5b: all patches in the two host species site, site S. Fig 5-5c: Patches of the annual host species only, G. tsugae, from the two host species site, site S. Fig 5-5d: Patches of the perennial host species only, G. applanatum, from the two host species site, site S.

117 One Host Species Site Two Host Species Site Population Data Total Num. Beetles Marked 772 1193 Total Num. Females Marked 441 747 Total Num Lg. Males Marked 206 254 Total Num Sm. Males Marked 125 190

Proportion Female 0.57 0.63 Proportion Lg. Male 0.27 0.21 Proportion Sm. Male 0.16 0.16

Dispersal Data Total Num. Movers 158 176 Total Female Movers 83 119 Total Lg. Male Movers 52 34 Total Sm. Male Movers 23 23

Proportion Movers 0.20 0.15 Proportion of Movers Female 0.53 0.68 Proportion of Movers Lg. Male 0.33 0.19 Proportion of Movers Sm. Male 0.15 0.13

Proportion of Females Moving 0.19 0.16 Proportion of Lg. Males Moving 0.25 0.13 Proportion of Sm. Males Moving 0.18 0.12

Table 5-1: Summary information on the number of beetles marked in the one host species site, Site R, and the two host species site, Site S. The first half of the table includes information about the total number of beetles marked in each site, and the representation of females, large horned males, and small horned males in those marked individuals. The second section of the table provides information about the number of beetles observed to have moved from one local population to another, and the breakdown of these individuals into the categories of females, large horned males, and small horned males.

118 Observed Expected From – To vs. From - To From – To vs. From - To p-value All dispersers P to P: 89 P to A: 22 P to P: 83.25 P to A: 27.75 0.211

A to A: 40 A to P: 30 A to A: 25.2 A to P: 44.8 0.0002

Females P to P: 68 P to A: 15 P to P: 62.25 P to A; 20.75 0.145

A to A: 22 A to P: 12 A to A: 12.14 A to P: 21.86 0.0004

All Males P to P: 25 P to A: 13 P to P: 28.5 P to A: 9.5 0.189

A to A: 16 A to P: 4 A to A: 7.14 A to P: 12.86 3.57e-05

Lg Horned P to P: 14 P to A: 9 P to P: 17.25 P to A: 5.75 0.117 Males A to A: 10 A to P: 4 A to A: 5 A to P: 9 0.0053

Sm Horned P to P: 11 P to A: 4 P to P: 11.25 P to A: 3.75 0.88 Males A to A: 6 A to P: 0 A to A: 2.14 A to P: 3.86 0.001

Table 5-2: Chi Square values for movement of dispersers between two host types. Expected, or null values were calculated from the real landscape experienced by the beetles and with the assumption that when individuals disperse, they disperse to their nearest neighbor. P stands for the perennial host, Ganoderma applanatum, and A stands for the annual host, Ganoderma tsugae.

119 Site R Site S median min max median min max All 29.07 1.41 211.40 4.00 550.81 Dispersers Female 1.41 1.41 170.66 32.8 4.00 550.81

Lg Males 54.33 1.41 196.49 27.31 5.10 550.81

Sm Males 1.41 1.41 211.40 47.42 9.22 503.61

Table 5-3: Summary information on the dispersal distances (meters) of forked fungus beetles in the single host species landscape, site R, and the two host species landscape, site S. Median distance dispersed, as well as minimum and maximum distances are reported. The categories represented in the table are all beetles observed to have dispersed, all female dispersers, all large horned male dispersers, and all small horned male dispersers.

120 intercept slope Rsquare P value Site R: Leaving 2.01 0.56 0.18 0.022 Patches Site R: Entering 1.49 0.64 0.15 0.04 Patches Site S: Leaving 1.17 0.28 0.43 2.36e-06 Patches Site S: Entering 1.65 0.24 0.37 1.64e-05 Patches

Table 5-4: Summary of linear regressions in Figures 3a-d: The effect of patch size on immigration and emigration. Site R is the single host species site, containing patches of only Ganoderma applanatum. Site S is the two host species site, with patches of both G. applanatum and G. tsugae.

121 Donate Join Site median range median range Site R 1 0 to 10 2 0 to 8

Site S 2 0 to 7 1.5 0 to11

Site S Perennial 1.5 0 to 7 2 0 to 11

Site S Annual 2.5 0 to 5 1 0 to 6

Table 5-5: Summary information on the number of local populations that donate individuals to a single local population and the number of local populations that dispersers from a single population join. Median number of local populations and range of local populations (minimum and maximum) are reported

122 Intercept Slope Rsquare P value Site R -0.102 1.03 0.52 1.04e-05

Site S 0.99 0.55 0.39 1.14e-05

Site S Annuals 1.81 0.42 0.26 0.065

Site S Perennials 0.58 0.61 0.46 6.69e-05

Table 5-6: Summary of linear regressions in Figures 5a-d. The correlation between the number of populations contributing dispersers to a single population and the number of populations a single population contributes dispersers to. Site R is the single host species site and Site S is a two host species site. Annual refers to the host species, Ganoderma tsugae, and Perennial refers to the host species, Ganoderma applanatum.

123 Appendix A:

SUPPORTING MATERIAL FOR CHAPTER 2

This appendix provides the code used for the dynamic state variable model in

chapter 2. Below is the code for both of the annual host quality functions as well as

whether the female starts on an annual or a perennial host patch. The code was run for

multiple values of the perennial host quality, hp, varying from 0.1 to 0.9. The code was

also run for many values of the probability of finding the annual host in the landscape,

proba. This was looped over values from 0.1 to 0.9 as well.

(1) Annual Host Decreasing Linearly in Quality over time and Female Starts on Annual Host

#mortality u<-0.05 #time steps T<-10 #eggs E<-5 #quality of annual host ha<-c(0, 0.25, 0.5, 0.75, 1, 1, 1, 0.75, 0.5, 0.25) #quality of perennial host hp<-0.1 #mortality associated with moving umove<-0.05 #prob of finding annual host (1-pa) is prob finding perennial proba<-0.1

#making the fitness matrix fmat<-matrix(NA, nrow=T, ncol=(E+1)) fmat[1,]<-c(0,0,0,0,0, 0) fmat[,1]<-rep(0, T)

#making the decision matrix cmat<-matrix(NA, nrow=T, ncol=(E+1)) for(i in 2:T){ for(j in 2:(E+1)){ #lay on annual currently on a<-fmat[(i-1), (j-1)]+ha[i]*(1-u)

124 #move and lay on new annual n<-fmat[(i-1), (j-1)]+ha[i]*(1-(u+umove))*proba #move and lay on perennial p<-fmat[(i-1),(j-1)]+hp*(1-(u+umove))*(1-proba) #don’t lay b<-fmat[(i-1), j]+0*(1-u) fmat[i,j]<-max(a,n,p,b) cmat[i,j]<-which(c(a,n,p,b)==max(c(a,n,p,b)))[1] } } fmat[1,2]<-NA fmat[(1:2), 3]<-NA fmat[(1:3), 4]<-NA fmat[(1:4), 5]<-NA fmat[(1:5), 6]<-NA fmat[(6:10), 1]<-NA fmat[(7:10), 2]<-NA fmat[(8:10), 3]<-NA fmat[(9:10), 4]<-NA fmat[10, 5]<-NA cmat[1,2]<-NA cmat[(1:2), 3]<-NA cmat[(1:3), 4]<-NA cmat[(1:4), 5]<-NA cmat[(1:5), 6]<-NA cmat[(6:10), 1]<-NA cmat[(7:10), 2]<-NA cmat[(8:10), 3]<-NA cmat[(9:10), 4]<-NA cmat[10, 5]<-NA

(2) Annual Host Decreasing Linearly in Quality over time and Female Starts on Perennial Host

#mortality u<-0.05 #time steps T<-10 #eggs E<-5 #quality of annual host ha<-c(0, 0.25, 0.5, 0.75, 1, 1, 1, 0.75, 0.5, 0.25) #quality of perennial host hp<-0.1 #mortality associated with moving umove<-0.05

125 #prob of finding annual host (1-pa) is prob finding perennial proba<-0.1

#making the fitness matrix fmat<-matrix(NA, nrow=T, ncol=(E+1)) fmat[1,]<-c(0,0,0,0,0, 0) fmat[,1]<-rep(0, T)

#making the decision matrix cmat<-matrix(NA, nrow=T, ncol=(E+1)) for(i in 2:T){ for(j in 2:(E+1)){ #lay on perennial currently on p<-fmat[(i-1), (j-1)]+hp*(1-u) #move and lay on annual a<-fmat[(i-1), (j-1)]+ha[i]*(1-(u+umove))*proba #move & lay on new perennial n<-fmat[(i-1),(j-1)]+hp*(1-(u+umove))*(1-proba) #don't lay b<-fmat[(i-1), j]+0*(1-u) fmat[i,j]<-max(a,n,p,b) cmat[i,j]<-which(c(a,n,p,b)==max(c(a,n,p,b)))[1] } } fmat[1,2]<-NA fmat[(1:2), 3]<-NA fmat[(1:3), 4]<-NA fmat[(1:4), 5]<-NA fmat[(1:5), 6]<-NA fmat[(6:10), 1]<-NA fmat[(7:10), 2]<-NA fmat[(8:10), 3]<-NA fmat[(9:10), 4]<-NA fmat[10, 5]<-NA cmat[1,2]<-NA cmat[(1:2), 3]<-NA cmat[(1:3), 4]<-NA cmat[(1:4), 5]<-NA cmat[(1:5), 6]<-NA cmat[(6:10), 1]<-NA cmat[(7:10), 2]<-NA cmat[(8:10), 3]<-NA cmat[(9:10), 4]<-NA cmat[10, 5]<-NA

126 (3) Annual Host Increasing and then Decreasing in Quality Over Time and Female Starts on Annual Host

#mortality u<-0.05 #time steps T<-10 #eggs E<-5 #quality of annual host ha<-c(1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1) #quality of perennial host hp<-0.1 #mortality associated with moving umove<-0.05 #prob of finding annual host (1-pa) is prob finding perennial proba<-0.1

#making the fitness matrix fmat<-matrix(NA, nrow=T, ncol=(E+1)) fmat[1,]<-c(0,0,0,0,0, 0) fmat[,1]<-rep(0, T)

#making the decision matrix cmat<-matrix(NA, nrow=T, ncol=(E+1)) for(i in 2:T){ for(j in 2:(E+1)){ #lay on annual currently on a<-fmat[(i-1), (j-1)]+ha[i]*(1-u) #move and lay on new annual n<-fmat[(i-1), (j-1)]+ha[i]*(1-(u+umove))*proba #move & lay on perennial p<-fmat[(i-1),(j-1)]+hp*(1-(u+umove))*(1-proba) #don't lay b<-fmat[(i-1), j]+0*(1-u) fmat[i,j]<-max(a,n,p,b) cmat[i,j]<-which(c(a,n,p,b)==max(c(a,n,p,b)))[1] } } fmat[1,2]<-NA fmat[(1:2), 3]<-NA fmat[(1:3), 4]<-NA fmat[(1:4), 5]<-NA fmat[(1:5), 6]<-NA fmat[(6:10), 1]<-NA

127 fmat[(7:10), 2]<-NA fmat[(8:10), 3]<-NA fmat[(9:10), 4]<-NA fmat[10, 5]<-NA cmat[1,2]<-NA cmat[(1:2), 3]<-NA cmat[(1:3), 4]<-NA cmat[(1:4), 5]<-NA cmat[(1:5), 6]<-NA cmat[(6:10), 1]<-NA cmat[(7:10), 2]<-NA cmat[(8:10), 3]<-NA cmat[(9:10), 4]<-NA cmat[10, 5]<-NA

(4) Annual Host Increasing and then Decreasing in Quality Over Time and Female Starts on Perennial Host

#mortality u<-0.05 #time steps T<-10 #eggs E<-5 #quality of annual host ha<-c(1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1) #quality of perennial host hp<-0.1 #mortality associated with moving umove<-0.05 #prob of finding annual host (1-pa) is prob finding perennial proba<-0.1

#making the fitness matrix fmat<-matrix(NA, nrow=T, ncol=(E+1)) fmat[1,]<-c(0,0,0,0,0, 0) fmat[,1]<-rep(0, T)

#making the decision matrix cmat<-matrix(NA, nrow=T, ncol=(E+1)) for(i in 2:T){ for(j in 2:(E+1)){ #lay on perennial currently on p<-fmat[(i-1), (j-1)]+hp*(1-u) #move and lay on annual a<-fmat[(i-1), (j-1)]+ha[i]*(1-(u+umove))*proba

128 #move & lay on new perennial n<-fmat[(i-1),(j-1)]+hp*(1-(u+umove))*(1-proba) #don't lay b<-fmat[(i-1), j]+0*(1-u) fmat[i,j]<-max(a,n,p,b) cmat[i,j]<-which(c(a,n,p,b)==max(c(a,n,p,b)))[1] } } fmat[1,2]<-NA fmat[(1:2), 3]<-NA fmat[(1:3), 4]<-NA fmat[(1:4), 5]<-NA fmat[(1:5), 6]<-NA fmat[(6:10), 1]<-NA fmat[(7:10), 2]<-NA fmat[(8:10), 3]<-NA fmat[(9:10), 4]<-NA fmat[10, 5]<-NA cmat[1,2]<-NA cmat[(1:2), 3]<-NA cmat[(1:3), 4]<-NA cmat[(1:4), 5]<-NA cmat[(1:5), 6]<-NA cmat[(6:10), 1]<-NA cmat[(7:10), 2]<-NA cmat[(8:10), 3]<-NA cmat[(9:10), 4]<-NA cmat[10, 5]<-NA

129 Vita: Carrie Anne Schwarz

Ph.D. 2006 Pennsylvania State University, Intercollege Program in Ecology, University Park, PA. Advisors: Dr. Ottar N. Bjørnstad and Dr. Michael C. Saunders. Title of Thesis: The Role of Habitat Heterogeneity in Population Dynamics: From Individual Behavior to Metapopulation Structure.

M.S. 2001 Pennsylvania State University, Department of Entomology, University Park, PA. Advisor: Dr. Michael C. Saunders. Title of Thesis: Aquatic Insect Community Structure and Function in Ephemeral Streams in Central Pennsylvania.

B.S. 1998 University of Maryland Baltimore County, Department of Biology, and Department of Geography.

Publications

McManaman, C. 2001. Aquatic Insect Community Structure and Function in Ephemeral Streams in Central Pennsylvania. Master's Thesis, Pennsylvania State University, Department of Entomology.

Awards & Grants 6/2005 Vartkes Miroyan Memorial Award 11/2004: Brian Horton Memorial Award 5/2004: NSF Doctoral Dissertation Improvement Grant 8/2002: Departmental Travel Award (Yendol Travel Grant) 10/2002: Ralph O. Mumma Graduate Award 2001: Viticultural Consortium Grant

Teaching Experience Fall 2003: Served as Teaching Assistant for Introduction to Population Dynamcis Spring 2000: Taught Field Crop Entomology, as well as designed and graded lectures, exams, and assignments. Fa1999 & Spr 2000: Taught four lab sections (two per semester) of Introduction to Entomology Fall 1999: Taught Forest Entomology (Lab), as well as designed and graded lectures, exams, and assignments associated with lab Fall 1998: Taught Introductory Biology Lab, as well as designed and graded lectures, exams, and assignments associated with lab