[sharon k. collinge, chris ray] disease eco

Disease Ecology

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Disease Ecology

Community structure and pathogen dynamicsedited by

Sharon K. Collinge

and

Chris RayUniversity of Colorado, USA

1

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British Library Cataloguing in Publication DataData available

Library of Congress Cataloging-in-Publication DataDisease ecology / edited by Sharon K. Collinge and Chris Ray.

p. cm.Includes bibliographical references.ISBN 0198567081 (alk. paper) ISBN 0198567073 (alk. paper)1. Communicable diseasesEnvironmental aspects. 2. Host-parasite

relationshipsEnvironmental aspects. 3. Communicable diseases in animalsEnvironmental aspects. I. Collinge, Sharon K. II. Ray, Chris.

[DNLM: 1. Communicable Diseasesepidemiology. 2. Ecology.3. Host-Parasite Relations. WA 110 D6113 2006]RA643.D57 2006614.5dc22

2005020774

Typeset by Newgen Imaging Systems (P) Ltd., Chennai, IndiaPrinted in Great Britainon acid-free paper by Antony Rowe, Chippenham, Wiltshire

ISBN 0198567073 9780198567073ISBN 0198567081 (Pbk.) 9780198567080 (Pbk.)

10 9 8 7 6 5 4 3 2 1

To all the creatures that delight and inspire

Preface

Our research on disease ecology began severalyears ago when our collaborative research groupwas awarded funding from the NSF/NIH jointprogram, Ecology of Infectious Diseases to studylandscape change and disease dynamics in prairiedogs. Many of the ideas and relationships that ledto this book were generated through the annual PInetwork meetings for this program. We have learnedvolumes from the participants in this group andespecially appreciate the support and vision ofJoshua Rosenthal (NIH Fogarty Center) and SamScheiner (NSF) in fostering collaborative interac-tions among a diverse group of scientists interestedin ecology and infectious disease dynamics.

The idea for this book arose at the 2002 meeting ofthe Ecological Society of America, where we wereinspired by the growing number of presentations ondisease ecology. We followed up on this trend byorganizing a symposium explicitly focused oncommunity ecology and epidemiology for the 2003ESA meeting in Savannah. As two ecologists withexperience in population, community, and land-scape ecology, we sought to gather the collectiveexpertise of ecologists working at the interface ofdisease ecology and epidemiology. The symposiumin Savannah was highly successful (despite thedelays incurred because participants had to catch aferry to reach our 8 a.m. symposium!) and we wereencouraged by Ian Shermans enthusiasm for arelated book project through Oxford University Press.We contacted additional contributors to add to thebreadth of topics covered, and began soliciting thechapters that you see in this volume.

We especially thank our prairie dog diseaseresearch group for stimulating our thoughts andengaging discussions regarding disease ecology,including Jack Cully, Ken Gage, Whit Johnson,Michael Kosoy, Andrew Martin, and Bai Ying, as wellas the graduate students in the group, Ellen Bean,Jory Brinkerhoff, David Conlin, Tammi Johnson, RyanJones, Kimberly Kosmenko, Amy Markeson, SueRodriguez-Pastor, and Bala Thiagarajan. We wouldhave made little progress without the enthusiasticwork of numerous field assistants over the past3 years, and we sincerely thank them all.

This book benefited greatly from careful reviewand discussion of chapters by an informal Diseaseecology discussion group at the University ofColorado-Boulder, including Ellen Bean, JoryBrinkerhoff, David Conlin, Ryan Jones, MatthewKern, Amy Markeson, Andrew Martin, SusanPerkins, and Jamie Voyles. We are grateful for theircontributions and are happy to report that many ofthe comments and insights made by this group foundtheir way into the final versions of these chapters.

No serious project is completed without theunderstanding and endless patience of those peopleclose to us. So a million thank-yous to Joan and Jefffor the emotional support, for being there with theChipotle burritos, and for putting up with the latenights and early mornings.

Finally, we are sincerely grateful to Ian Sherman atOxford for his continued patience, encouragement,and support of this project, and to Kerstin Demata,Abbie Headon and Anita Petrie for expert answersto our seemingly endless supply of questions.

V I I

Contents

Contributors xi

1 Community epidemiology 1Sharon K. Collinge and Chris Ray

2 Extending the principles of community ecology to address the epidemiology of hostpathogen systems 6Robert D. Holt and Andrew P. Dobson

3 Community ecology meets epidemiology: the case of Lyme disease 28Richard S. Ostfeld, Felicia Keesing, and Kathleen LoGiudice

4 Microbial community ecology of tick-borne human pathogens 41Keith Clay, Clay Fuqua, Curt Lively, and Michael J. Wade

5 Disease dynamics in plant communities 58Charles E. Mitchell and Alison G. Power

6 Host selection and its role in transmission of arboviral encephalitides 73Robert S. Unnasch, Eddie W. Cupp, and Thomas R. Unnasch

7 Freshwater community interactions and malaria 90Eliska Rejmnkov, John Grieco, Nicole Achee, Penny Masuoka, Kevin Pope, Donald Roberts, and Richard M. Higashi

8 The community ecology of Vibrio cholerae 105Kathryn L. Cottingham and Julia M. Butzler

9 Food webs and parasites in a salt marsh ecosystem 119Kevin D. Lafferty, Ryan F. Hechinger, Jenny C. Shaw, Kathleen Whitney, and Armand M. Kuris

10 Shifting roles of abiotic and biotic regulation of a multi-host parasite following disturbance 135Mary F. Poteet

11 Urbanization and disease in amphibians 153David K. Skelly, Susan R. Bolden, Manja P. Holland, L. Kealoha Freidenburg, Nicole A. Freidenfelds, and Trent R. Malcolm

I X

x C O N T E N T S

12 Spatial-temporal dynamics of rabies in ecological communities 168Leslie A. Real and James E. Childs

13 The emergence of Nipah and Hendra virus: pathogen dynamics across a wildlife-livestock-human continuum 186Peter Daszak, R. K. Plowright, J. H. Epstein, J. Pulliam, S. Abdul Rahman, H. E. Field, A. Jamaluddin, S. H. Sharifah, C. S. Smith, K. J. Olival, S. Luby, K. Halpin, A. D. Hyatt, A. A. Cunningham, and the Henipavirus Ecology Research Group (HERG)

14 Potential effects of a keystone species on the dynamics of sylvatic plague 202Chris Ray and Sharon K. Collinge

Index 217

List of contributors

X I

Nicole Achee, Department of Preventive Medicine andBiometrics, USUHS, 4301 Jones Bridge Rd., Bethesda,MD 20814, USA. E-mail: [email protected]

Susan R. Bolden, School of Forestry & EnvironmentalStudies, Yale University, New Haven, CT 06511, USA. E-mail: [email protected]

Julia M. Butzler, Department of Biological Sciences,Dartmouth College, Hanover, NH 03755, USA. E-mail: [email protected]

James E. Childs, Department of Epidemiology, Yale School of Medicine, Yale University, New Haven,CT 06510, USA. E-mail: [email protected]

Keith Clay, Department of Biology, Indiana University, Bloomington, IN 47405, USA. E-mail: [email protected]

Sharon K. Collinge, Department of Ecology and Evolutionary Biology and Environmental StudiesProgram, University of Colorado, Boulder, CO 80309-0334, USA. E-mail: [email protected]

Kathryn L. Cottingham, Department of BiologicalSciences, Dartmouth College, Hanover, NH 03755,USA. E-mail: [email protected]

Andrew A. Cunningham, Institute of Zoology,Zoological Society of London, Regents Park, LondonNW1 4RY, U.K. E-mail: [email protected]

Eddie W. Cupp, Department of Entomology andPlant Pathology, Auburn University, Auburn, AL36849, USA. E-mail: [email protected]

Peter Daszak, Consortium for Conservation Medicine,460 West 34th Street, New York, NY 10001, USA. E-mail: [email protected]

Andrew P. Dobson, Department of Ecology andEvolutionary Biology, Princeton University, Princeton,NJ 08544, USA. E-mail: [email protected]

Jon H. Epstein, Consortium for Conservation Medicine,460 West 34th Street, New York, NY 10001, USA. E-mail: [email protected]

Hume E. Field, Animal Research Institute, Dept. of Primary Industries, LMB 4, Moorooka 4105, Brisbane, Queensland, Australia. E-mail: [email protected]

L. Kealoha Freidenburg, School of Forestry &Environmental Studies, Yale University, New Haven, CT 06511, USA. E-mail: [email protected]

Nicole A. Freidenfelds, Department of NaturalResources, University of New Hampshire, Durham, NH 03824, USA. E-mail: [email protected]

Clay Fuqua, Department of Biology, Indiana University,Bloomington, IN 47405, USA. E-mail: [email protected]

John Grieco, Department of Preventive Medicine andBiometrics, USUHS, 4301 Jones Bridge Rd., Bethesda,MD 20814, USA. E-mail: [email protected]

Kim Halpin, CSIRO, Livestock Industries, Australian Animal Health Laboratory, Private Bag 24,Geelong, Victoria 3220, Australia. E-mail: [email protected]

Ryan F. Hechinger, Department of Ecology, Evolutionand Marine Biology, University of California, Santa Barbara, CA 93106, USA. E-mail: [email protected]

Richard M. Higashi, Environmental Chemistry Group,Crocker Nuclear Laboratory, University of California,Davis, CA 95616, USA. E-mail: [email protected]

Manja P. Holland, School of Forestry & EnvironmentalStudies, Yale University, New Haven, CT 06511, USA.E-mail: [email protected]

Robert D. Holt, Department of Zoology, University ofFlorida, Gainesville, FL 32611-8525, USA. E-mail: [email protected]

Alex D. Hyatt, CSIRO, Livestock Industries, AustralianAnimal Health Laboratory, Private Bag 24, Geelong,Victoria 3220, Australia. E-mail: [email protected]

xii L I S T O F C O N T R I B U TO R S

Abdul Aziz bin Jamaluddin, Deputy Director General,Department of Veterinary Services, Ministry ofAgriculture, 8th & 9th Floor, Wisma Chase Perdana,Off Jalan Semantan, Damansara Heights, 50630 KualaLumpur, Malaysia. E-mail: [email protected]

Felicia Keesing, Biology Department, Bard College,Annandale-on-Hudson, NY 12504, USA. E-mail: [email protected]

Armand M. Kuris, Department of Ecology,Evolution and Marine Biology and Marine ScienceInstitute, University of California, Santa Barbara, CA93106, USA. E-mail: [email protected]

Kevin D. Lafferty, USGS Western Ecological ResearchCenter and Marine Science Institute, University ofCalifornia, Santa Barbara, CA 93106, USA. E-mail: [email protected]

Curt Lively, Department of Biology, Indiana University, Bloomington, IN 47405, USA. E-mail: [email protected]

Kathleen LoGiudice, Biology Department, Union College, Schenectady, NY 12308, USA. E-mail:[email protected]

Stephen Luby, Programme on Infectious Diseases andVaccine Sciences, ICDDR,B, Centre for Health andPopulation Research, GPO Box 128, Mohakhali, Dhaka 1212, Bangladesh. E-mail: [email protected]

Trent R. Malcolm, School of Forestry & EnvironmentalStudies, Yale University, New Haven, CT 06511, USA.E-mail: [email protected]

Penny Masuoka, Department of PreventiveMedicine and Biometrics, USUHS, 4301 Jones Bridge Rd., Bethesda, MD 20814, USA. E-mail: [email protected]

Charles E. Mitchell, Department of Biology andCurriculum in Ecology, University of North Carolina, Chapel Hill, NC 27599, USA. E-mail: [email protected]

Kevin J. Olival, Center for Environmental Research and Conservation and Department of Ecology,Evolution, and Environmental Biology, ColumbiaUniversity, New York, NY 10027, USA. E-mail: [email protected]

Richard S. Ostfeld, Institute of Ecosystem Studies,Millbrook, NY 12545, USA. E-mail: [email protected]

Raina K. Plowright, University of California, Davis,One Shields Avenue, Davis, CA 95616, USA. E-mail: [email protected]

Kevin Pope, Geo Eco Arc Research, Aquasco, MD 20608, USA. E-mail: [email protected]

Mary F. Poteet, Section of Integrative Biology, TheUniversity of Texas at Austin, Austin, TX 78712-0183,USA. E-mail: [email protected]

Alison G. Power, Dept. of Ecology and EvolutionaryBiology, Cornell University, Ithaca, NY 14853-2701,USA. E-mail: [email protected]

Juliet Pulliam, Dept. of Ecology & Evolutionary Biology,Princeton University, Princeton, NJ 08544, USA. E-mail: [email protected]

Sohayati Abdul Rahman, Virology Section, Veterinary Research Institute, No 59, Jalan SultanAzlan Shah, 31400 Ipoh, Perak Darul Ridzuan,Malaysia. E-mail: [email protected]

Chris Ray, Department of Ecology and Evolutionary Biology, University of Colorado,Boulder, CO 80309-0334, USA. E-mail: [email protected]

Leslie A. Real, Department of Biology, Center forDisease Ecology, Emory University, Atlanta, GA 30322, USA. E-mail: [email protected]

Eliska Rejmnkov, Department of Environmental Science and Policy, University ofCalifornia, Davis, CA 95616, USA. E-mail: [email protected]

Donald Roberts, Department of PreventiveMedicine and Biometrics, USUHS, 4301 Jones Bridge Rd., Bethesda, MD 20814, USA. E-mail: [email protected]

Syed Hassan Sharifah, Veterinary Research Institute, No 59, Jalan Sultan Azlan Shah, 31400 Ipoh, Perak Darul Ridzuan, Malaysia. E-mail: [email protected]

Jenny C. Shaw, Department of Ecology, Evolutionand Marine Biology, University of California,Santa Barbara, CA 93106, USA. E-mail: [email protected]

David K. Skelly, School of Forestry & EnvironmentalStudies and Department of Ecology & EvolutionaryBiology, Yale University, New Haven, CT 06511, USA.E-mail: [email protected]

Craig S. Smith, Dept. of Primary Industries, LMB 4,Moorooka 4105, Brisbane, Queensland, Australia. E-mail: [email protected]

Robert S. Unnasch, Sound Science LLC, Boise, ID 83702, USA. E-mail: [email protected]

Thomas R. Unnasch, Division of Geographic Medicine, University of Alabama at Birmingham,Birmingham, AL, 35294-2170, USA. E-mail: [email protected]

Michael J. Wade, Department of Biology, IndianaUniversity, Bloomington, IN 47405, USA. E-mail: [email protected]

Kathleen Whitney, Department of Ecology, Evolution and Marine Biology, University ofCalifornia, Santa Barbara, CA 93106, USA. E-mail: [email protected]

1CHAPTER 1

Community epidemiologySharon K. Collinge and Chris Ray

1.1 The raison dtre of this book

Many infectious diseases of recent concern haveemerged from complex ecological communities,involving multiple hosts and associated parasites.Several of these diseases appear to be affected byanthropogenic impacts at trophic levels below orabove the host community, which suggests thatdisease prevalence may be altered in unanticipatedways by changes in the structure of ecological com-munities. Predicting the epidemiological ramifica-tions of such alteration in community compositionshould be a primary goal of community ecologistsand provides a justification for strengthening thecurrent union between community ecology andepidemiology. The purpose of this book is to high-light exciting recent advances in theoretical andempirical research toward understanding the import-ance of community structure in the emergence ofinfectious diseases. To that end, this book has onedominant message: studies of epidemiology can beapproached from the perspective of communityecology, and students of community ecology cancontribute to epidemiology.

To date, most research on hostparasite systemshas explored a limited set of communityinteractions, including a community of host speciesinfected by a single parasite species, or a commun-ity of parasites infecting a single host. Less effort hasbeen devoted to addressing additional complica-tions, such as multiple-hostmultiple-parasitesystems, sequential hosts interacting on differenttrophic levels, alternate hosts with spatially varyinginteractions, effects arising from trophic levels otherthan those of hosts and parasites, or stochasticeffects resulting from small population size in at

least one alternate host species. Many of these issuesare addressed in this book, by studies that linkcommunity structure with pathogen transmissionand disease dynamics.

This book follows several excellent recentvolumes focused on aspects of disease ecology.Chief among these is Ecology of Wildlife Diseases(OUP, 2002), which emphasizes ecology andevolution of wildlife diseases, primarily at thescales of individuals and populations. Parasites andthe Behavior of Animals (OUP, 2002) describes howparasitism may alter behavior of individualanimals, with significant consequences for popula-tion and community dynamics. Evolution ofInfectious Disease (OUP, 1994) forges links betweenevolutionary biology and medicine. ConservationMedicine (OUP, 2002) bridges gaps betweenecosystem health, animal health, and humanhealth. Our book on Disease Ecology is unique in itsexplicit emphasis on theory and empirical researchin community ecology in relation to infectiousdiseases.

We believe that the timing of this book is ideal, asit corresponds with a new synthesis of theory incommunity ecology and epidemiology beingdeveloped by Robert Holt, Andrew Dobson, andtheir colleagues. Their exciting work illustratesepidemiological applications for many of thefamiliar theoretical tools of community ecology.These tools are opening inroads to the study of verycomplex hostpathogen communities. We begin thebook (Chapter 2) with these new perspectives oncommunity epidemiology and, wherever possible,illustrate how new analytical tools can be appliedwithin the studies discussed in this book.

We expect that this book will engage a broadaudience, including scholars active in the fields ofcommunity ecology and epidemiology, medicalpractitioners, and advanced undergraduates, post-graduates and land managers with some back-ground in ecology or epidemiology. Each chapterrelates to an area of theory in community ecologyand illustrates how community-level processesoperate in a particular study system to affectpathogen transmission and disease dynamics.Chapters explore several areas of theory incommunity ecology (e.g. predatorprey dynamics,keystone species effects, succession, disturbance,species invasions, diversity and stability). In orderto foster consistency among chapters, we askedeach contributor to consider the following threequestions in preparing their chapter:

What key concepts from community ecology arebest illustrated in your study system? How do community interactions appear tocontribute significantly to pathogen transmissionand disease prevalence in your study system? What sorts of community complexity must beconsidered to effectively predict disease dynamics?

1.2 Some useful definitions

The chapters in this book should be accessible tomost readers familiar with basic concepts inecology and epidemiology. We provide here a briefglossary, in alphabetical order, of some commonlyused terms to serve as introduction to the reader orjust to refresh your memory.

Enzootic refers to an infectious disease that ispresent in a host population at all times, but havinglow incidence within the population.

Epizootic refers to an infectious disease outbreakin a population that affects a large number ofanimals simultaneously but does not persist.

An infectious disease refers to the change in thestate of health of a host organism as a result ofinvasion of the body by pathogenic organisms.Note that the disease is the manifestation of theinfection in a host organism, but infection can occurwithout causing disease. In this book, infectiousagents include viruses, bacteria, fungi, protozoans,and multicellular endoparasites.

A pathogen is any disease-producing microorgan-ism or material (e.g. prions are infectious proteins,but are not technically organisms).

A parasite is an organism that lives in (endopara-sites) or on (ectoparasites) the living tissue of a hostorganism; the biological interaction between hostand parasite is called parasitism. Microparasites,which include viruses and bacteria, reproducewithin their hosts. Macroparasites, which includemulticellular endo- and ectoparasites, generallyspend some portion of the life cycle away from theprimary host.

The basic or intrinsic reproductive rate, R0, of amicroparasite is usually defined as the number ofinfective hosts (secondary infections) resultingfrom a single infective host (primary infection). Formicroparasites, R0 depends primarily on hosthostor hostvectorhost contacts. For macroparasites,R0 is usually defined as the number of adult parasitesproduced by a single adult parasite. In both cases,R0 applies only when parasites are rare. For R0 < 1,the parasite does not increase when rare.

A reservoir host is an animal species thatmaintains a parasite life cycle and functions as thesource of the infection for humans or other species.

SEIR models have been used in humanepidemiology for decades, and more recently havebeen incorporated into wildlife disease models.These models describe disease dynamics within apopulation of Susceptible individuals, Exposedindividuals, Infectious individuals, and Recovered(or resistant) individuals. Variations on thismodeling framework include, for example, SISmodels which apply to diseases with a rapid onsetof the infectious period and no legacy of resistance.

A zoonosis is an infection or disease that can beshared between humans and wildlife. Many of thediseases discussed in these chapters are zoonoses.

1.3 How this book works

To emphasize our message that community ecologyand epidemiology can be productively linked, eachchapter contains two boxes that highlight key toolsand concepts in community epidemiology. One boxfocuses on techniques that will be useful to otherresearchers studying community aspects of infec-tious diseases. The second box emphasizes aspects of

2 D I S E A S E E C O L O G Y

disease transmission from the perspective ofcommunity structure. With these boxes, we intend toprovide our readers with the theoretical frameworkand empirical tools necessary to design and evaluatestudies that effectively address the communitycontext of infectious diseases.

1.4 Chapter highlights and connections

Each of the chapters in this book provides a novelcontribution to the understanding of diseasedynamics within complex communities. Mostchapters relate shifts in disease dynamics toalterations of community structure driven byanthropogenic activities, including intensiveagriculture (Chapters 7 and 13), clear-cut forestry(Chapter 10), urbanization (Chapter 11), or habitatloss and fragmentation (Chapters 3 and 14).Although the authors expertise spans a wide rangeof study systems, from microbial communitiesinside ticks to the continental spread of rabidomnivores, all emphasize the reciprocal interactionsbetween host communities and disease emergence.Most of the chapters involve ecological field studiesof diseases just emerging in the United States. Thisfocus on emergent diseases and their communityecology is clearly applicable to disease dynamicsworldwide.

To begin, Holt and Dobson (Chapter 2) note thatmost emerging diseases involve more than onehost, and many involve fairly complex hostpathogen communities. Understanding thesediseases requires a framework in which to examinethe dynamics of pathogens that infect two or morehost species. These authors explore epidemiologyof hostpathogen communities through analysis ofcommunity modules, or small sets of interactingspecies. They adapt familiar tools from communityecology to analyze pathogen transmission dynamicsin several types of communities, and develop aseries of simple models to elucidate the dynamicsof parasite coexistence. Holt and Dobsons chapterprovides a very useful conceptual foundation andintroduction to analytical tools that can be appliedto many hostpathogen systems. One application isexplored by Ray and Collinge (Chapter 14), whoconsider potential effects of a keystone species onthe dynamics of plague in the United States.

The functional role of species richness incommunities has been the subject of much recentresearch in ecology, and several chapters in thisvolume explore this topic in the context of diseasedynamics. For example, Ostfeld et al. (Chapter 3)note that both mammal species richness andspecies composition are critical to prevalence ofLyme disease in northeastern US forests. Theauthors detailed field studies and modeling effortsdemonstrate that species-specific functional rolescan depend on the composition of the remainingcommunity. This theme is considered again inChapter 5 by Mitchell and Power in a very differentstudy system involving plant pathogens, but themessage that more diverse communities may havereduced pathogen loads is consistent. Both chaptersalso discuss the importance of pathogenspillover, whereby high pathogen loads on onespecies within the community can result in higherpathogen prevalence in other species. The presenceof this one species clearly can have dramatic effectson disease dynamics within the community. In thecase of Lyme disease, its the white-footed mouse,and in the case of barley yellow dwarf virus, itswild oats.

Also relevant to the dynamics of Lyme disease,Clay et al. (Chapter 4) explore the intriguingpossibility that microbial communities within ticksmay influence expression of disease. These authorshave found that microbes pathogenic to humans areoften relatively rare within these communities,suggesting that the prevalence of human diseasemay be affected by community interactions betweenpathogenic and non-pathogenic microbes within thevectors themselves. Moreover, they draw fromecological theory on assembly rules to consider theabundance and distribution of microbes within ticks.These authors have discovered a surprising diversityof microbes within individual ticks, suggesting agreat opportunity to use these (highly replicated)microbial communities to test several concepts incommunity ecology that have proven difficult to testin larger (less replicated) systems.

Several highly virulent, arthropod-borne viruses(arboviruses) that cause serious human neurolog-ical diseases, such as West Nile encephalomyelitisand St Louis encephalomyelitis, are transmitted bymosquitoes that feed on both wildlife and human

C O M M U N I T Y E P I D E M I O L O G Y 3

hosts. The recent emergence of these viruses hasprompted investigation into those mosquito speciesand wild birds that are conspicuously involved intransmission cycles. Unnasch et al. (Chapter 6)describe an innovative molecular technique toidentify the source of mosquito blood meals, whichis proving to be quite useful in discerning the rolesof particular vector and host species. Oneinteresting result is their discovery that reptiles alsohost arboviruses and may play a critical role inmaintaining these pathogens over the winter. Theyalso use this molecular technique to determine thevectorial capacity of different mosquito species.Vectorial capacity is a term that takes into accountthe efficiency of the vector in transmitting thepathogen, the lifespan of the infectious hosts, andthe degree of contact between the host and vector.Because species interactions are of primeimportance in determining vectorial capacity, anunderstanding of community ecology is clearlyessential for elucidating the epidemiology ofvectored diseases.

Rejmnkov et al. (Chapter 7) further discussvectorial capacity in the context of malaria trans-mission in Belize. These authors describe hownutrient enrichment from intensive agriculturalactivities leads to dramatic shifts in wetland plantcommunities (to marshes dominated by cattails),which in turn cause changes in the abundance ofparticular mosquito species involved in malariatransmission. Their results show that the mosquitospecies most favored by these plant communitychanges is also the most capable vector of malariain their study area. Moreover, a less capable vector,which was the more abundant mosquito speciesbefore nutrient enrichment, also survives well incattail marshes. Thus, this shift in the marsh com-munity may result in higher overall vector density,perhaps causing increased risk of malaria forhumans.

Nutrient enrichment of aquatic systems can alsopromote conditions that favor growth of pathogenicmicroorganisms. For example, nutrient additionmay directly increase the growth rate of Vibriocholerae, the causative agent of cholera, or mayenhance the growth of plankton that serve asattachment substrates for the bacteria (Cottinghamand Butzler, Chapter 8). Until quite recently, there

were no techniques available to examine thecommunity ecology of microorganisms. Not surpris-ingly, little is known about how interactions amongbacterial strains or species, for example, may affectrelative abundance or expression of pathogenicityin infectious bacteria. Clearly, there is a greatopportunity for research on the roles of keyinterspecific interactions, such as competition andpredation, in affecting bacterial abundance,morphology, and possibly virulence. Cottinghamand Butzler report on the current understanding ofthe environmental context in which V. choleraedynamics unfold, and suggest that communityecology provides an excellent conceptual founda-tion for understanding both the pathogen and thedisease it causes.

Aquatic ecosystems are often foci of diseasecaused by both microparasites (as in cholera,above) and macroparasites, because water is anexcellent medium for survival and transmission ofparasites, and because aquatic systems are oftenheavily modified by humans. Three chapters of thisvolume (Chapters 911) provide excellent examplesof community influences on macroparasite trans-mission in three very different aquatic systems.Lafferty et al. (Chapter 9) extend a long history offood web studies in community ecology by addingparasites to an exceptionally detailed description ofa salt-marsh food web. The authors show thatincluding parasites in the food web changesconsiderably the metrics calculated for typical foodwebs. For example, food web connectance (theaverage proportion of other species with whicheach species interacts), increases dramatically infood webs where parasites are included, comparedwith where they are excluded. This finding hasprofound implications for our understanding ofcommunity structure and function.

Both Poteet (Chapter 10) and Skelly et al.(Chapter 11) examine how human modification ofaquatic systems affects rates of macroparasitism.Poteet studied how forestry practices influenceeach host and parasite stage of a trematode parasitethat infects giant salamanders in the northwesternUnited States. The complex life cycle of thismacroparasite includes stoneflies, snails, and sala-manders, which constitute key components ofaquatic food webs in small streams. Completion of


the parasite life cycle depends on predation ofstoneflies by salamanders, and Poteet shows howthe rate of transmission depends critically on thepredatorprey functional response curve. Further,habitat disturbance due to clear-cut forestry altersthe form of this functional response, with intrigu-ing consequences for parasite transmission. On theopposite US coast, Skelly et al. (Chapter 11) examinehow urbanization and associated habitat modifica-tion influence the prevalence of macroparasites intwo common amphibian species, green frogs andspring peepers. Across two parasite taxa and thesetwo host species, the authors detected no effect ofurbanization on infection prevalence. However,they did find evidence of severe parasitism withinthree of the 16 wetlands studied; interestingly, thesewere the most heavily human-modified wetlandsin the study and supported the highest densities ofthe snails that act as intermediate hosts for theseparasites (echinostomes). Skelly et al. conclude thatparasite infections are clearly context dependent,and suggest that it would be productive to examinea greater number of species in these communities todetermine why emergence is likely in particularcontexts.

Animal movement is notoriously difficult tostudy, yet is clearly a critical component of thedynamics of many diseases. Chapters 12 (Real andChilds) and 13 (Daszak et al.) incorporate animalmovement explicitly into studies of viral transmis-sion and spread. In their well-studied example,Real and Childs use spatially explicit models toshow that movement of rabies across largegeographic areas is impeded by landscape featuressuch as rivers. This suggests that rivers may slowmovement of raccoons and therefore providebarriers for movement of rabies. Very little isknown about the impacts of rabies on vertebratecommunities, or about how host species composi-tion may affect transmission rates. Because verteb-rate species composition varies spatially andtemporally, it is likely that there are combinationsof mammalian assemblages that create higher orlower rates of risk for rabies transmission tohumans and domestic animals.

The emergence of Nipah virus in Malaysia(Chapter 13) involves interactions between domestic

livestock (pigs), two native species of frugivorousflying foxes, and several species of fruiting trees.Satellite telemetry allowed Daszak et al. to reject thehypothesis that drought-related changes in flying-fox movement patterns led to the emergence ofNipah virus. On the contrary, their movement datasuggest that Nipah virus is present continually inpeninsular Malaysia, and was therefore likely to beavailable for introduction into pigs prior to thelarge drought that occurred in the mid-1990s.Additionally, their study of Nipah virus indicatesthat interspecific interactions affect viral preval-ence. Fruiting trees planted next to hog contain-ment facilities provide feeding and roosting sitesfor fruit-eating bats that harbor the virus. Thesesites provide opportunities for pathogen spilloverfrom bats to pigs, and ultimately to humans. Theauthors ability to link the presence of fruiting trees at hog farms to Nipah virus emergence has led to livestock management plans that specifybuffer zones at pig farms where fruit trees areexcluded.

As editors of this volume, we have had theopportunity to review the concepts covered in each chapter of this book, and to apply theseconcepts where possible to our own studies ofdisease. In the final chapter (Ray and Collinge,Chapter 14), we use this opportunity to discuss an old disease in several new ways. We discussplague as an emergent disease of wildlife in thewestern US, noting the potential feedbacks between plague dynamics and communitystructure. Plague has caused much local extinction of the black-tailed prairie dog, which isarguably a keystone of prairie ecology. We suggestthe added potential for key effects of the prairie dog on plague dynamics, using the models of Holtand Dobson (Chapter 2) to explore how prairiedogs may mediate both direct and apparentcompetition among alternate hosts of plague. Thetake-home message from our study is similar tothat of most studies in this book: the key tounderstanding zoonoses is understandinginterspecific transmission.

This should prove to be a useful book. Turn thepage and start reading, because the world needsyour contributions to community epidemiology.

C O M M U N I T Y E P I D E M I O L O G Y 5

2.1 Background

Community ecologists grapple with the structureand dynamics of ensembles of species that live inthe same habitat, landscape, or region, and sopotentially interact (Morin 1999; Lawton 2000). Thisconcern with interspecific interactions has led tosustained interest in several questions. One is tounderstand how species within a single trophiclevel, competing for the same resources, manage tocoexist (Chesson 2000a; Holt 2001). Other centralconcerns of community ecology go beyond coex-istence within a trophic level to include topics suchas understanding the structure and dynamics offood webs, and the relationship between diversityand ecosystem stability (e.g. McCann 2000). Inaddressing these issues, including the core issue ofcoexistence, parasites are increasingly recognized as hidden but vital constituents of natural com-munities (Morand and Arias-Gonzalez 1997;Thompson et al. 2001). In turn, there is a growingappreciation of the community dimensions of infec-tious disease epidemiology, as witnessed by thechapters of this volume.

In applied ecology, understanding the communitycontext of infectious disease is critically important(e.g. zoonotic diseases, Ostfeld et al. 2001 and Chapter3, this volume; invasive species, Mitchell and Power2003; conservation, McCallum and Dobson 1995;Woodroffe 1999; Lafferty and Gerber 2002; Torchin etal. 2003). Such understanding can improve our abilityto interpret and mitigate the emergence of novelinfectious diseases (Daszak et al. 2000; Woolhouse2002). Community structure can influence disease

emergence in many ways. Parasites can infect multi-ple host species, and most hosts are vulnerable toinfection by multiple parasite species (Dobson andFoufopoulos 2001, Morgan et al. 2004). Even special-ist hostpathogen interactions are embedded in com-plex food webs, generating complex feedbacks. Forinstance, a generalist predator with a nonlinear func-tional response to a prey species that itself harbors aspecialist parasite can lead to cyclic or chaotic pat-terns of disease prevalence (Hall et al. 2004; Holt, inpress; Holt et al., in review). Maintenance of naturalhostparasite dynamics may be crucial for maintain-ing species diversity and facilitating successionaldynamics (Gilbert 2002); anthropogenic disruption(e.g. species introductions) can potentially lead tocascading shifts in hostparasite interactions, withdevastating effects on community structure.

A key issue at the interface of community ecologyand infectious disease epidemiology is how theinterdependence of hosts and parasites affectsspecies coexistence. Many processes can influencecoexistence, often in idiosyncratic ways, yet coexist-ence is most broadly understood as arising from theinterplay of three factors (Chesson 2000a; Holt2001): (1) the inherent properties of the speciesthemselves (e.g. feeding specializations), (2) proper-ties of the extrinsic environment (e.g. abundance offood resources), and (3) the dynamic impacts eachspecies in turn has upon the environment (e.g. influ-ence of feeding on future food resources). Trade-offsbetween species are usually required for coexistence(Kneitel and Chase 2004). For instance, consider twoconsumer species competing via exploitation for

6

CHAPTER 2

Extending the principles of communityecology to address the epidemiologyof hostpathogen systemsRobert D. Holt and Andrew P. Dobson

two discrete, renewable food resources (see, forexample, Chase and Leibold 2003). For stable com-petitive coexistence, where each species can increasewhen rare, the two species have to differ in terms oftheir responses to resource availabilityan exampleof niche differentiation. Moreover, the two resourcesin turn need to respond differently to the twoconsuming species, such that the two consumerspecies experience distinct feedback effects arisingfrom their impacts upon the resource supply.Finally, the abundance and renewal rates of theresources cannot be too different; otherwise, despitethe existence of niche differences, one species will beable to exclude the other.

When analyzing coexistence, it is often insufficientto consider just pairs of species and their requiredresources in a local environment over shorttimescales. Interactions with other trophic levelscan permit coexistence (as in keystone predation oncompeting species, Holt et al. 1994), or preclude it(as in apparent competition between prey species,Holt 1977; Holt and Lawton 1994). Noncompetitiveinteractions such as facilitation can moderate theimpact of competition. Spatial flows of resourcesand species are often central to maintaining speciescoexistence at broader spatial scales (e.g. Holt 1993;Leibold and Miller 2004), as is temporal variation inresource availability or environmental conditions,when species have different responses to suchvariation (Chesson 2000a). Increasing the temporal,spatial, and trophic scales of inquiry broadens therange of trade-offs, environments, and feedbacksthat can permit (or preclude) coexistence (Chesson2000a; Holt 2001).

All of these questions in community ecology bearon the issue of understanding the persistence ofmultispecies assemblages of parasites and theirhosts. This is a broad and rapidly evolving topic(see prior syntheses in Grenfell and Dobson 1995;Hudson et al. 2002). Because of the large numbers ofspecies in communities, and the complexity andfluidity of the interaction webs that link thesespecies, understanding community dynamics is asubstantial challenge, even ignoring parasites andinfectious disease! One fruitful approach tounraveling the dynamics of complex food webs isthe analysis of community modules (Holt 1997a;Persson 1999). A community module is a carefully

chosen multispecies extension of pairwise interac-tions, chosen because the configuration of interac-tions is found in a wide range of species assemblages.Several familiar modules at the heart of communityecology are shown in Fig. 2.1. Figure 2.2 displayssimilar community modules involving infectiousdiseases. The community modules that havereceived greatest attention are those elucidating thecoexistence of species competing for resources.Because hosts provide resources, habitats, anddispersal mechanisms for parasites, models fromclassical community ecology can be used to elu-cidate controls on parasite community structure.Toward this end, we will consider here severalmodels based on simple epidemiological modulesthat address coexistence. We first focus on coex-istence of parasites, then turn to the issue of howparasites influence host coexistence.

E X T E N D I N G T H E P R I N C I P L E S O F C O M M U N I T Y E C O L O G Y 7

Figure 2.1 Multispecies assemblages involving a relatively smallnumber of species interacting in a defined way provide a usefulconceptual waystation between pairwise interactions and highlycomplex food webs (after Holt 1997a). Note that some of thesecommunity modules may go by different names depending on theoutcome of interactions; for example, predation on competing preymay be referred to as keystone predation if it facilitates preycoexistence, or predator-mediated competition if it excludes oneprey species (resulting in a food chain).

Consumers

Resource

Predator

Prey

Resource

Predator

Prey1 2

Prey

Predator

Resource

Top predator

Intermediate

Resource

Consumers

Resources

Intraguild predation

Predation on competing prey

Apparent competitionFood chain

Community modules in ecology

Exploitative competition

Niche partitioning

2.2 The host community: a templet for the parasite community

A theme of growing interest (and the focus ofseveral chapters in this book) is that parasites canbe key determinants of host population dynamicsand community organization (e.g. Anderson andMay 1978; Hudson et al. 1998; Dobson 1999;Hudson et al. 2002). For many ecologists (includingourselves), the dramatic impacts of parasites upontheir hosts provide the principal raison dtre forstudies of parasitehost ecology. However, forsome purposes it may be useful to consider the hostcommunity as an arena in which parasite dynamicsand interactions play out, without a significantreciprocal affect on host community structure. Inthe felicitous phrase of Jaenike and Perlman (2002),at times parasites are only a kind of trophicgarnish on the food web.

The trophic garnish perspective on parasitesmay approximate reality in many cases. Humanhosts, for instance, harbor large numbers (at leastthousands) of microbial species, mostly bacteria (B. Bohannan and M. McFall-Ngai, personal com-munication). Some bacteria are always pathogenic,

and others can be pathogenic in particular circum-stances (e.g. due to wounding or stress), but it issurely true that many of these potential parasitesusually have only a negligible impact on theirhuman hosts, in effect being commensals that donot impact host fitness (Levin and Antia 2001).Similarly, nematodes in Drosophila may havenegligible effects on host fecundity (Perlman andJaenike 2003). Competitive interactions withinindividual hosts (e.g. chemically mediated interfer-ence among nematodes) can provide densitydependence that regulates parasites, even if hostfecundity and mortality are not affected (Jaenike1998; Poulin 1998). Density-dependent resourcecompetition or facultative host defenses may reduceparasite survival or reproduction, without alteringhost demography (Shostak and Scott 1993; Jaenike1996). An assumption of fixed host numbers is oftenmade in classical human epidemiology, such as theKermackMcKendrick (1927) model; such modelsin effect assume that the combined determinants ofhost abundance are effectively independent ofpathogen impacts. Even harmful pathogens mayfail to influence host population size if host popula-tion growth is density-dependent. For instance,damping disease may reduce seedling survival ina plant cohort (Augspurger 1983), but if the numberof survivors exceeds the number of adult plants thatcan be sustained on a site, the overall course of plantpopulation dynamics may be unchanged. Finally,many parasites that can in principle regulate hostnumbers do so only in certain contexts (e.g. wherehosts experience stressful conditions, Brown et al.2003; Lafferty and Holt 2003).

By assuming that total host numbers are fixed,independent of parasite load, one can identifykey mechanisms of coexistence acting primarilyon the parasites. Once understood, these mechan-isms provide a useful yardstick for interpretingthe consequences of relaxing the assumption offixed host abundances. Given that the host com-munity provides a templet for parasite dynamics,one can then ask how the properties of this templetinfluence patterns of parasite community organ-ization and richness. This broad question is thefocus of much work in classical parasitology(reviewed in Price 1980; Poulin 1998), but wesuggest it would be useful to revisit this questionmore specifically using the conceptual framework


Figure 2.2 Many problems at the interface between communityecology and infectious disease epidemiology can be viewed asmodules analogous to those in Fig. 2.1.

Parasites

Host

Hyperpathogen

Pathogen

Host

Parasite

Hosts1 2

Hosts

Parasite

Resource

Parasites

Hosts

Predation on hosts

Keystone parasitism

Shared parasitismParasite chain

Niche partitioning

Community modules in epidemiology

Predator

Healthy host

Infectedhost

Parasite competition

provided by contemporary community ecology. Herewe sketch some potentially fruitful applicationsof community ecology, using simple, illustrativemodels.

As noted above, a fundamental concern of com-munity ecology is to understand how the strengthand pattern of interspecific interactions influencespecies coexistence. Classical community ecologyfocused on equilibrial properties of species interac-tions in closed ecosystems in stable environments.If each of a set of consumer species is limited by theabundance of a single, depletable, limiting factor(as in the exploitative competition module of

Fig. 2.1), then at equilibrium at most one species isexpected to persist (Levin 1970). For instance, in awell-mixed chemostat where algae compete for asingle nutrient, theory predicts (and experimentsconfirm) that a single species of algae dominates,driving inferior competitors to extinction (Tilman1982; Grover 1997). If multiple species stably coex-ist, one or more of the conditions required for com-petitive exclusion must be violated (Chesson 2000a;Holt 2001). We can categorize mechanisms of coex-istence in terms of how they differ from this simplelimiting case. Box 2.1 provides one typology ofcoexistence mechanisms.


Box 2.1 A prcis of mechanisms of coexistence

Several key mechanisms of coexistence have been identifiedby community ecologists. These mechanisms dependgenerally on differences in how species exploit resources,and how the environment responds to such exploitation(Chesson 2000a; Holt 2001). In practice, these mechanismsare neither sharply defined nor mutually exclusive. See thetext for further exploration of several scenarios ofcoexistence among competing parasites.

A. Coexistence in closed, equilibrial communities

Some systems (e.g. terrestrial communities on remote islands)are essentially closed to immigration, and may experiencelittle temporal variation. Much of classical community theoryassumes such closed, equilibrial communities.

Classical niche partitioning. In a stable environment withpopulations at equilibrium, species coexistence requiresniche partitioning (e.g. Schoener 1989). Models of nichepartitioning assume: (1) the environment is heterogeneous,with multiple potential limiting factors (e.g. aheterogeneous resource base), and (2) species havedifferent requirements (e.g. due to trade-offs in exploitativeability for distinct resources). Such niche differences arenecessary but not sufficient for coexistence; species mustalso be similar in how they respond to general abioticfactors (the equalizing conditions of Chesson 2000a),and have differential impacts upon the limiting factorsthemselves (Chase and Leibold 2003). Often, localcoexistence is permitted by subtle variation in microhabitatsand in the selection of microhabitats by individuals (e.g.Kotler and Brown 1999).

Localized interactions between individuals. Even in spatiallyhomogeneous communities, dispersal followingreproduction can be spatially circumscribed. This increasesthe impact of intraspecific competition relative tointerspecific competition, potentially facilitating coexistenceat local scales (Bolker and Pacala 1999).

Food-web effects. Frequency-dependent consumption bynatural enemies (e.g. due to predator switching) canpromote competitive coexistence. If superior competitorsfor resources are more vulnerable to predation, coexistencecan occur (see the keystone predation module in Fig. 2.1;Holt et al. 1994; Leibold 1996; Chase and Leibold 2003). Ifan inferior resource competitor can directly prey upon thesuperior competitor, coexistence may be permitted (see theintraguild predation module; Holt and Polis 1997).

Nontrophic mechanisms of population regulation. Manybiological mechanisms can influence population regulation,and thus interactions among species. For instance, ifsuperior exploiters also experience strong intraspecificinterference, this can permit the continued existence ofinferior competitors (Schoener 1976).

B. Closed, nonequilibrial communities

Although much of theoretical community ecology assumesthat the environment is constant, many natural systems in factexperience substantial temporal variation in abiotic conditionsand resource supply rates. Moreover, populations may haveintrinsically unstable dynamics, leading to cycles or chaoticdynamics even in constant environments. Given temporalvariability, other mechanisms of coexistence can operate.

continues

2.3 Mechanisms of coexistence in parasite assemblages

We think it is fair to assert that the relativeimportance of each potential coexistence mechanism(Box 2.1) is not well understood for any naturalcommunity; parasite communities are certainly noexception. However, there are hints in the literaturethat many of these mechanisms may influence thestructure of parasite assemblages. One basicquestion is the degree to which parasite speciesrichness reflects processes acting within individualhosts (dubbed the infracommunity in parasito-logy; Holmes 1973, Holmes and Price 1986; Goateret al. 1987, Poulin 1998), versus processes acting atthe level of entire host populations and communi-ties. Here we focus on the latter.

2.3.1 Simple models for microparasitecompetition and coexistence

Parasites compete for susceptible hosts and theresources those hosts contain. In principle, ana-logues of any mechanism in Box 2.1 could helpexplain parasite coexistence. There is considerableroom for developing theory that formalizes thesemechanisms in a manner specifically tailored tohostparasite systems. We sketch here severalmodels for microparasites (e.g. viruses, bacteria),illustrating different modes of coexistence.

Parasites abstractly compete at two levels oforganization: within individual hosts and betweenhosts. Microparasites establish populations withinindividual hosts. Because hosts provide resourcesthat can be consumed, this permits exploitative


Temporal niche partitioning (storage effects). In variableenvironments, coexistence may reflect temporal variation inthe performance of different species on the same resource.This mechanism requires devices to slow the rate ofpopulation decline during bad times, such as seed banks orlong-lived adult classes (Chesson 2000a). High recruitmentinto the seed bank or adult classes during better times canpermit the population to persist through poor times. Ifdifferent species are superior at different times, a largenumber of species with demographic storage effects canpotentially coexist on a shared resource.

Nonlinear dynamics. Often, population growth rates arehighly nonlinear functions of the magnitude of limitingfactors (e.g. resource availability), leading to unstabledynamics. If competing species have different nonlinearresponses to shared limiting factors, nonequilibrialcoexistence may occur (Grover 1997; Huisman andWeissing 1999; Chesson 2000a; Abrams and Holt 2002).More subtle mechanisms of coexistence involving nonlineardynamics reflect shifts between different dynamical regimesin systems with cyclical or chaotic dynamics (e.g. Harrisonet al. 2001).

C. Open communities

Many natural communities are open, coupled to an external landscape or regional species pool via dispersal

(Polis et al. 2004; Holyoak et al. 2005). This permits the operation of a broad range of coexistence mechanisms involving species movement patterns inresponse to spatial and temporal variation (Holt 1993;Chesson 2000b).

Migration and habitat selection. Some species (e.g. migratorybirds) may circumvent (and exploit) temporal variation bymigration or seasonal habitat selection. If different speciesare regulated at different seasons and/or in differenthabitats, they can potentially coexist. Moreover, speciesthat disperse at different rates in effect average over spatialvariation in different ways; this subtle effect can at timespermit coexistence (McPeek and Holt 1992; Debinski et al.2001).

Metapopulation processes. Coexistence may occur if inferiorcompetitors can rapidly colonize and establish populationsfollowing disturbance, permitting the exploitation oftransient habitats, whereas superior competitors dispersemore sluggishly. This mechanism requires recurrent loss ofthe superior competitor from local sites (e.g. throughdisturbance).

Source-sink dynamics. An inferior competitor may persist inone community if it is a superior competitor in a nearbycommunity. Dispersal from source habitats then permitssustained presence in what would otherwise be sinkhabitats for the inferior competitor (Holt 1993; Leibold andMiller 2004).

Box 2.1 continued

competition between multiple species that co-occurwithin a patch (individual host). For the moment,assume that only a single parasite species can persistin an individual host, and that whichever speciesinitially colonizes that host excludes other species;that is, there is no coinfection by multiple speciesleading to either within-host coexistence orsuperinfection (where one parasite can supplantanother). The absence of coinfection is assumed inmany theoretical studies (e.g. Dushoff and Dwyer2001). (We will relax this assumption below.)

Even if coinfection can at times occur, one cansafely ignore it in a number of plausible circum-stances. Consider a system in which two parasitespecies with similar birth and death rates use thesame tissues within a host that does not exhibitspecies-specific immune responses (Iwasa et al.2004). If infective propagules are small and infre-quent, then whichever species first colonizes(infects) an individual host is likely to exclude theother species. After the first species has achieved anequilibrium between births and deaths within itshost, small and infrequent propagules of a secondspecies cannot invade unless its birth or death ratesare relatively favorable. Species with similar birthand death rates will generally be distributed in acheckerboard pattern, with each host infected byjust one of these similar parasite species. This out-come of parasite competition for host tissues withinan individual parallels that of competition for hostindividuals within a population, as modeled inEquation (2.1) below. In other systems, assumingno coinfection can be viewed as a simple limitingcase of a more complex epidemiological model. Forinstance, if coinfection rapidly leads to host mortal-ity, few host individuals will in practice be coin-fected. Alternatively, if encounter rates are low,hosts infected by any single parasite species arelikely to recover or die before encountering a hostinfected with another parasite. Finally, for ourpurposes, assuming no coinfection is conceptuallyuseful, because it permits us to identify variousmechanisms operating at the host level to influenceparasite coexistence.

Assuming two parasite species and no coinfec-tion, we can divide a host population into one unin-fected class and two, nonoverlapping classes ofindividuals infected with either parasite 1 or

parasite 2. Recall that for the moment we areassuming the host is regulated at its carrying capac-ity, K, by factors other than parasitism. A simpleSI model describing susceptible and infectedhosts and the exploitative competition between twospecies of microparasite spread by density-depend-ent transmission includes the following terms forthe dynamics of each parasite:

(2.1)

Here, S is the density of susceptible hosts, Ii thedensity of hosts infected with parasite species i, ithe transmission rate for parasite species i, and dithe rate of parasite loss from the host population(including death and recovery of infected hosts).The second equation states that total host numbersare fixed at carrying capacity by factors other thanparasitism.

When parasite i occurs alone, the first equation inmodel (2.1) reveals that the equilibrial density ofsusceptible hosts is S* di /i. Parasite j can invadeif dIj /dt > 0; that is, if j S* dj > 0 or di /i > dj /j. Ifthis is true, when parasite j is alone at equilibrium,parasite i cannot invade. Thus, model (2.1) predictscompetitive exclusion, and the winning parasite isthe one that can persist at the lowest density of sus-ceptible hosts. This model parallels in its essentialfeatures standard resourceconsumer models(Grover 1997), which formalize the idea that, atequilibrium, a single species will dominate anysingle limiting resource. Here, the resource is thesusceptible host subpopulation. See Allen et al.(2004) for more complex epidemiological models ofthis type.

If we substitute S K I1 I2 into the first equa-tion in (2.1), and do this for each of the two parasitespecies, we generate a competition model ofLotkaVolterra form, with nonintersecting zero-growth isoclines, corresponding to competitiveexclusion. This very simple model predicting com-petitive exclusion provides a springboard for morecomplex models that illuminate how differentaspects of host population and community proper-ties influence parasite coexistence. Next, we sketchsome plausible scenarios corresponding to the coex-istence mechanisms of Box 2.1. Due to limitations in

dIdt

S d I i

K S I I

ii i i= =

= + +

( ) , , 1 2

1 2


space and in the current state of theory, we considersome potential mechanisms in more detail thanothers; these mechanisms are not necessarily moreimportant in natural communities.

2.3.1.1 Classical niche partitioningIn a multispecies host community, if each parasitespecies is specialized to a different host species, andthe hosts do not themselves compete, parasite coex-istence is trivial, as it is determined entirely by theindependent responses each parasite has to its ownhost (e.g. each respective host should exceed thethreshold density for its parasite). In effect, thisscenario assumes a rigid niche partitioning amongparasites. But in many natural systems, parasitesare shared by multiple host species (Cleaveland et al. 2001; Dobson 2004; Woolhouse 2002; Woolhouseet al. 2001), and hosts harbor multiple parasites. Ina species-rich host assemblage, heterogeneityamong parasites in how they use different hostspecies can in principle permit the sustained coex-istence of multiple species of parasites.

There is now a rich theoretical literature on thedynamics of multi-host, one-pathogen systems (e.g.Holt and Pickering 1985; Bowers and Begon 1991;Begon et al. 1992; Begon and Bowers 1995; Bowersand Turner 1997; Greenman and Hudson 2000;Dobson 2004). Understanding cross-species trans-mission can be of great importance for addressingapplied issues, and ignoring such transmission canlead to erroneous conclusions. For instance, Hess(1994) provocatively argued that increasing con-nectivity in a metapopulation might not always bea helpful conservation strategy, because connectiv-ity also facilitates movement of pathogens. Severalauthors have noted that this result may be moot iftransmission is generally facilitated by alternative,reservoir hosts (Gog et al. 2002; McCallum andDobson 2002).

A simple listing of known hosts does not quant-ify the dynamical importance of multiple hostspecies for parasite dynamics and coexistence.Compilations of parasite host range conflate sev-eral alternative dynamical scenarios. First, cross-species transmission may be only of historical orbiogeographic importance. For HIV, contact withthe original source host (presumably an Africanprimate) was historically crucial but is now irrelevant

in determining the subsequent dynamics of thedisease in humans. Many emerging diseases ofeconomically important plants involve singleintroductions, not recurrent infection within asingle community (Anderson et al. 2004). Second, inthe case of recurrent cross-species transmission, theincidence of the disease in a focal host can be influ-enced by the presence, abundance, and epidemio-logical properties of alternative hosts. In this case, itis useful to distinguish several alternative scenarios(elaborating on a suggestion by Antonovics et al.2002; see also May et al. 2001):

1. The focal host species may be a permanentdemographic sink for the parasite, in that each prim-ary infection in the focal host generates less thanone secondary infection within its own population(R0 < 1). Under this scenario, focal host infectionsare generally due to spillover of the parasite froma source host. Reciprocal transmission back to thesource host (Antonovics et al. 2002) may alterprevalence in both source and sink hosts. This scen-ario is particularly likely when no host speciesalone is sufficiently dense to sustain the infection.2. The focal host species may be able to sustain theparasite entirely on its own (R0 > 1), but recurrentinfection from alternative hosts may nonethelesssignificantly perturb dynamics within the focal hostpopulation.3. As an intermediate case, the focal host may be anintermittent sink, such that R0 varies through time.Alternative hosts may then be particularly import-ant for ensuring parasite persistence through timesof low R0 in the focal host. In some ways, this scen-ario is reminiscent of the migration and habitatselection mechanism in Box 2.1.

Density-dependent disease transmission impliesa threshold host population size, below which R0 < 1.A host may be a sink for a parasite not because ofthe poor physiological suitability of the host, butbecause of ecological factors influencing hostabundance or background mortality rates, such asmicrohabitat or resource availability, predation, orcompetition with other species. In model (2.1)above, the rate at which an uninfected individualbecomes infected is proportional to the density of infectives in the host population. In some sexuallytransmitted or vector-transmitted diseases, however,


the rate of infection depends upon the frequency ofinfection in the host population (i.e. the fraction ofindividuals infected). With pure frequency-dependent disease transmission, there is not athreshold host population density. But more real-istic models of frequency-dependent transmissionsuggest that density-dependence often emerges atsufficiently low host numbers (Antonovics et al. 1995).So, it is likely that a threshold host density describesa wide range of infectious disease systems.

With recurrent cross-species infection, determin-ing the criterion for microparasite invasion (R0 > 1)requires a more complex approach than consider-ing R0 in each host alone (Dobson and Fofopoulos2001; Holt et al. 2003; Dobson 2004). Here weexemplify one approach, and extend it to considercompetition and effective niche partitioning amongparasite species.

We can generalize model (2.1) to include twoparasite and two host species, as follows:

(2.2)

The first two equations describe dynamics ofparasite 1 in host species 1 and 2; the second twoequations (with primes) describe parasite 2. Thequantity ij denotes transmission of infection frominfected individuals of species j to susceptible indi-viduals of species i; i scales loss rates (mortalityplus clearance or recovery) of infected individualsof host species i.

To complete the model, we must describedynamics of the rest of the host populations. Asbefore, we assume each host is regulated by strongdensity dependence (e.g. territoriality) independ-ent of parasitism, so Ki Si Ii Ii, i = 1,2. [Notethat with this assumption about host regulation, wepreclude apparent competition (Holt 1977; Holtand Pickering 1985; Hudson and Greenman 1998;Bowers 1999). We turn to such indirect interactionsbetween hosts below.] Alternative models include

exponential or logistic growth of hosts, regulated atleast in part by parasitism. For example, Begon et al.(1992) assume dNi/dt = riNi(1 Ni /Ki ) iIi, i = 1,2,for each of two host species; the first term is logisticgrowth experienced by all individuals in species i,and the second denotes additional mortality expe-rienced by infected individuals. In Box 2.2, wedescribe how a model with fixed host density (2.2)leads to isoclines that describe qualitatively theconditions for invasion of each parasite species;these isoclines can then be used to characterizesome necessary conditions for parasite coexistence.

The zero-growth isoclines depicted in Box 2.2characterize, for a system of two fixed-density hostspecies, the densities of susceptible hosts that per-mit shared parasites to increase when rare. Withthis graphical tool in hand, we can now addresssome aspects of competitive coexistence withoutwallowing in complex algebra. Each parasite has itsown zero-growth isocline. By jointly plotting eachspecies isocline, one can qualitatively characterizenecessary conditions for coexistence of a pair ofparasite species competing for susceptible indi-viduals of two host species. If the isocline forparasite i lies entirely inside the isocline for parasitej, then i can invade the system and depresssusceptible host density enough to keep j out. Thus,a necessary condition for parasite coexistence is thatthe two isoclines cross. (To characterize sufficientconditions, one must also consider host properties,such as the degree of regulation of hosts byparasitism.)

Parasites can coexist through different types ofniche partitioning. Coexistence may be relatedeither to the capacity each parasite has for usingindividual hosts, or to the pattern of transmissionwithin and among host species. If parasite transmis-sion is similar within and among host species, thezero-growth isoclines are straight lines. In this case,for these isoclines to cross, each parasite speciesmust experience a lower loss rate in a different hostspecies (Fig. 2.3 (a) and (b)). For example, parasitesmay coexist if each can better resist clearance from adifferent host. Such niche differentiation may be dueto differential tolerance of induced or constitutivehost defenses. Alternatively, if parasites have equi-valent loss rates in both host species, they can coexistonly if each has its highest transmission rate in a


dIdt

S I S I I

dIdt

S I S I I

dIdt

111 1 1 12 1 2 1 1

222 2 2 21 2 1 2 2

1

= +

= +

== +

= +

11 1 1 12 1 2 1 1

222 2 2 21 2 1 2

S I S I I

dIdt

S I S I

II2


Box 2.2 A graphical model for parasite invasion: zero-growth isoclines

Before addressing coexistence in the communityrepresented by model (2.2), we need to characterizepersistence conditions for each parasite species alone. If weassume host abundance is fixed (e.g. each host is at itsrespective carrying capacity), we can ask whether a givenparasite (say species 1) can invade the host community. Todetermine the answer analytically, it is useful to rewrite theequations for parasite 1 (shown in main text) in the form

from which it is clear that the growth rate of the parasite isrepresented by the matrix. In fact, the asymptotic growthrate of the parasite is equivalent to the dominant eigenvalueof this matrix. The dominant eigenvalue equals zero (i.e.R0=1) when the determinant of this matrix (det[]) is zero,resulting in the following condition for zero parasite growth:

When this condition is plotted in relation to the densities ofsusceptible hosts (S1 and S2, as in Fig. 2.3), it describes thezero-growth isocline for the parasite. If the combination ofsusceptible host densities lies outside the isocline, then theparasite can invade; conversely, if susceptible host densitiesare between the isocline and the origin, the parasite willdecline toward extinction. The shape of the zero-growthisocline reflects the pattern of parasite transmission resultingfrom the type of interaction among host species (see Holt et al. 2003, and main text). In this particular model, host-specific loss rates, i, influence the intercepts, but not thecurvature of the parasites zero-growth isocline.

There is a subtle distinction between the zero-growthisoclines in Fig. 2.3 and the more familiar isoclines ofresourceconsumer theory (Grover 1997). In a typicalresourceconsumer model, the current growth rate of theconsumer is simply a function of current resourceabundance, with no explicit effect of time. In a hostparasitesystem, susceptible host abundance is indeed a resourcefor the parasite. But the growth rate in question here is theasymptotic growth rate for the parasite, after enough timehas passed following invasion for the parasite to settle intoits stable pattern of distribution across the host species.

The zero-growth isoclines derived above and depicted inFig. 2.3 generalize the concept of a minimum host densityto two host populations, and encapsulate graphically theminimal host community configurations permitting parasiteinvasion. One can also plot additional isoclines of constantR0 as a function of susceptible host densities (Fig. 2.4). Thezero-growth isocline is that set of susceptible host densitiesfor which R0=1; that is, although total host densities arefixed in model (2.2), parasites may regulate susceptiblehost densities. At equilibrium with a given parasite, thedensity of susceptible hosts will be depressed to levelssomewhere on the zero-growth isocline for that parasite.This equilibrium then determines the initial array ofsusceptible host densities that a second parasite specieswill face when it attempts to invade the host community.By plotting isoclines for each parasite simultaneously, wecan begin to characterize conditions for exclusion, versuscoexistence. With similar parasite transmission rates withinand among host species, the isoclines are straight lines(Fig. 2.3(b)). If most transmission is within host species, theisoclines instead bow out from the origin (Fig. 2.3(d)).Begon et al. (1999) analyzed transmission dynamics of thecowpox virus in mixed populations of bank voles and woodmice, and showed that despite their close co-occurrence,transmission between host species was negligible, soconvex isoclines are quite plausible in this system, anddoubtless many others as well.

In Fig. 2.3 and in model (2.2) in the text, both zero-growthisoclines have negative slope; this is not necessarily the casefor systems with frequency-dependent transmission, vector-mediated transmission, or free-living infectious stages (Holt etal. 2003; J.Antonovics, personal communication).Figure 2.4 compares the isoclines describing contours ofconstant R0 as a function of host density for several frequency-dependent systems, each with two host species.With multiple host species, frequency-dependent transmissioncan buffer disease outbreaks, leading to the dilution effectdescribed by Ostfeld and Keesing (2000), while density-dependent transmission usually leads to enhancedpotential for parasite establishment and outbreak.When the frequency-dependent case is expanded to explicitly considertransmission vectors for the parasite, then the height ofeach R0 contour varies approximately with the square root ofvector abundance (Dobson 2004). So increasing vectordensity increases the potential for an epidemic. This effecthelps explain why vector control has been so effective incontrolling diseases such as malaria and yellow fever.

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Figure 2.3 Examples of isocline shapes and coexistence for two parasite species competing for susceptible individuals of two (fixed-density) host species. In (a) and (b), transmission is similar within and between host species, but each parasite persists longer in a differenthost. In (c) and (d), parasite loss rates are similar between species, but there is more intra- than interspecific transmission. The time plots (a)and (c) are numerical simulations demonstrating that the parasites can coexist. In the isocline plots (b) and (d), the smaller dots indicateequilibrial densities of susceptible hosts when each parasite occurs alone; in these examples, the equilibria shift so as to facilitate invasion bythe other parasite. The specific parameters are as follows: (a) Total number of hosts infected by each parasite, starting with host 1 infected byeach parasite at a density of 0.001. 11 =12 =21 =22 =11 =12 =21 =22 = 1, 1 = 3, 2 = 2.5, 1 = 2, 2 = 3.5, K1 = 4, K2 = 5.(b). Isoclines for parameters in (a). When both parasites are present, the system approaches the point at which the isoclines cross. (c and d).Same as (a) and (b), but 11 = 0.5,12 = 0.1,21 = 0.4,22 = 0.8,11= 0.7,12= 0.3,21= 0.1,22= 0.5, 1 = 2 = 1= 2= 0.5,K1 = K2 = 1.

An important subtlety arises with pathogens that use ticksas vectors. The abundance of ticks may be tightly coupledto the abundance of their hostshosts that also harborthe pathogens that ticks transmit. In this case, increases inhost abundance may lead to both increased vectorabundance and enhanced amplification of diseasetransmission. If factors other than host availability can

regulate vector numbers when hosts are common,nonlinear isoclines can readily occur.

In systems where vector transmission is by mosquitoes ortsetse flies, whose abundance is independent of hostabundance, an increase in host numbers may dilute the percapita vector attacks and the per capita production of freshinfections from any given infected host (R0). Models that

continues

different host, corresponding to nonlinear isoclines(Fig. 2.3 (c) and (d)). Transmission rates can differ inthis way for a variety of reasons. Each parasitespecies may increase contact behavior in a differenthost species, or each may be specialized to exploitcontacts in a different host.

Crossing isoclines are necessary but not sufficientfor parasite coexistence. For coexistence, hostcarrying capacities cannot differ too greatly. A para-site that is better at exploiting a host with a lowcarrying capacity is vulnerable to exclusion by aparasite that is better at exploiting a host with ahigher carrying capacity. This effect arises in model(2.5) below, which is a limiting case of model (2.2).

It should be noted that in model (2.2), we assume that hosts that are susceptible to infectionby one parasite species in general are also

susceptible to the other parasite. If hosts havespecialized immune responses, then host indi-viduals that recover and become immune to one parasite species may still be available for infectionby a second parasite species. This leads to a kind of niche partitioning within a single hostspecies, which can facilitate the coexistence of competing parasites (Pej Rohani, personalcommunication).

2.3.1.2 Spatially localized competitionRoberts and Dobson (1995; see also Dobson 1985)explored a model for competing macroparasites thatare characteristically aggregated within the hostpopulation. This model incorporates both exploit-ative competition for hosts, and direct interference.Parasite aggregation can facilitate coexistence,


include a free-living pool of pathogens can generate isoclineswith positive slope (Holt et al. 2003). If different pathogenshave very different isoclines in these two-host systems, theinteractions between them may not resemble familiarcompetition isoclines at all.

The isocline approach to analyzing coexistence of parasitesin host communities provides useful insights, but it may bedifficult to apply to particular empirical systems. In naturalsystems it can even be difficult to show that thresholddensities exist (Begon et al. 2003). Detailed case studiesseem to reveal that parasite persistence is not governed somuch by average host density, as by the detailed spatialstructuring of infection processes (e.g. Keeling and Gilligan2000). An important task for future work is to articulate theimpact of alternative forms for transmission dynamics, andthe spatial structuring of transmission, on the coexistenceconditions for parasites potentially competing for the samesuite of host species.

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Figure 2.4 Zero-growth isoclines resulting from density-dependent (a) or frequency-dependent (b) transmission. Theseisoclines with nonlinear or positive slopes depict contours ofconstant R0 for a single parasite species infecting two hosts (seeDobson 2004). Were one to simultaneously plot isoclines for twoparasite species infecting these two hosts, it is clear that theresulting figures would not match those of classical competitiontheory (details not shown).

Box 2.2 continued

particularly given negative cross-species correlationsin the parasite distributions. The model of Robertsand Dobson (1995) assumed that hosts are solelyregulated by the parasites (which increased hostmortality). If, as in model (2.1), we instead assumethat host density is constant and regulated by factorsother than parasitism, the macroparasite equationspresented in Roberts and Dobson (1995, p. 194) can be re-written as the familiar LotkaVolterracompetition equations, with parameters such as thecompetition coefficient expressed as a function ofepidemiologically relevant quantities such asparasite fecundity and aggregation strength (detailsnot shown). This simplification of the model revealsthat the coexistence of competing macroparasitesrequires that they not be too dissimilar in theirinherent ability to use the host. For example,macroparasites with similar rates of growth whenrare may coexist via this equalizing mechanism(sensu Chesson 2000a). This simplification of themodel also reveals that competing macroparasitesmust differ in their patterns of aggregation. Ifaggregation patterns ensure that intraspecificinterference exceeds interspecific interference, thenmacroparasites may coexist via this stabilizingmechanism (sensu Chesson 2000a).

We are not aware of parallel theoretical studiesdirectly pertinent to microparasites. However, wenote that the degree of aggregation created in anyhost macroparasite system reflects the interplaybetween heterogeneities in host susceptibility(immunological, spatial, and genetic) and parasitevirulence. In general, parasites that are more viru-lent will exhibit lower levels of aggregation andthus will be less likely to coexist with other species(Shaw and Dabson 1998) (barring spatial hetero-geneities in parasite transmission efficiency, whichwould tend to promote parasite coexistence).

2.3.1.3 Frequency-dependent mortalityAs in community ecology in general, food-webinteractions may influence which parasite dominatesand whether there will be exclusion or coexistence,depending upon the detailed structure of thetrophic interactions (see Chapter 9, this volume, forfurther treatment of parasites in food webs). Inmodel (2.1), the loss rate of infected hosts implicitlyincorporates losses due to predation. In that case,

predation can potentially influence parasitecommunity structure if different parasite strainslead to different (fixed) predation rates for infectedhosts; for example, by affecting host behavior.

In models that allow the death rate of infectedhosts to increase with the density of infected hosts,coexistence may be promoted (Pugliese 2002).Assume that parasite species 1 has higher transmis-sion rates and also causes its host to attract generalistpredators, who respond facultatively to the abun-dance of the infected prey. We can modify model (2.1)to account for this effect by allowing infected hostdeaths to increase directly with their own abundance:

(2.3)

Now assume that parasite 2 has lower transmis-sion rates and does not affect host predation rates,so that host dynamics follow model (2.1). For sim-plicity, assume the death rate of hosts with parasite1 is a linear function of their density, d1(I1) = d1 + dI1,whereas hosts with parasite 2 have a fixed deathrate; moreover, assume that each parasite caninvade when alone with the host. The condition forparasite coexistence is:

The left-hand inequality describes when parasite 1can invade, given that parasite 2 is present and atequilibrium. The right-hand inequality describeswhen parasite 2 can invade, given that parasite 1 ispresent and at equilibrium. The full conditionreveals that coexistence is more likely with largerhost carrying capacity and larger effects of parasite1 on host predation rates.

Packer et al. (2003) and Ostfeld and Holt (2004),building upon earlier work by Dobson (1988) andLafferty (1992), have recently emphasized theimportance of predation as a factor governinghostparasite dynamics, even if predators actsimply as density-independent mortality agentsupon various classes of prey. There are manyreasons to focus on systems combining predationand parasitism. When predators attack bothhealthy and infected prey individuals, the full rela-tionship corresponds to intraguild predation (Holtand Polis 1997; see Fig. 2.1), because predators both

1

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compete with parasites for healthy hosts and inflictmortality upon parasitized hosts. Generalist toppredators in many communities seem particularlyat risk to anthropogenic impacts. If disruption ofnatural predatorprey interactions reduces preymortality rates, one indirect consequence could bethe unleashing of hostpathogen interactions present in lower trophic levels. This could involveboth increases in disease incidence in species that are already sustaining the pathogen, andspread to novel hosts (Packer et al. 2003; Ostfeldand Holt 2004; Hethcote et al. 2004; Hall et al. 2005;Holt, in press). If selective predation promotesparasite coexistence, as in model (2.3), predatorremoval may also bring certain parasites todominance.

An interesting example comes from studies byHudson and colleagues on the interplay betweengamekeepers, predators, grouse, and a parasiticnematode (Hudson et al. 1992, 1998). Predatorsselectively attack heavily infected grouse. The jobof a gamekeeper is to reduce predator numbers, inorder to increase the game available for hunters.Across sites, Hudson found that the percentage ofgrouse heavily infected with worms actuallyincreases with the density of gamekeepers! Thissuggests that although there may indeed be moregrouse where gamekeepers are doing their job,those grouse on average are wormier. This effectshould be expected whether or not predatorsdifferentially prey upon wormier grouse; withfewer predators, parasite-infested prey can simply live longer and generate more secondaryinfections.

We should caution that the impacts of predatorremoval upon disease dynamics may differ dra-matically from the descriptions above. For instance,predators may themselves be hosts, or be transmis-sion agents for the parasite (as is true in manycomplex life cycles, Parker et al. 2003; see alsoLafferty 1992 and Chapters 9 and 10, this volume).Predator removal may then disrupt transmissiondynamics and reduce disease incidence among theremaining hosts. Moreover, prey behavioralresponses to predation can alter t

[sharon k. collinge, chris ray] disease eco

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