Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T09:11:40.787Z Has data issue: false hasContentIssue false

Chapter Eight - Using insect baculoviruses to understand how population structure affects disease spread

from Part II - Understanding between-host processes

Published online by Cambridge University Press:  28 October 2019

By
Bret D. Elderd  and
Greg Dwyer
Edited by
Kenneth Wilson ,
Andy Fenton  and
Dan Tompkins
Kenneth Wilson
Affiliation:
Lancaster University
Andy Fenton
Affiliation:
University of Liverpool
Dan Tompkins
Affiliation:
Predator Free 2050 Ltd
Get access

Summary

Approaches to understanding infectious disease dynamics often emphasise studies of multiple hosts or pathogens, but for many diseases, population structure may play a more important role. Changes in population structure may stem from heterogeneity of infection risk in the host population, pathogen polymorphism, spatial structure, or host population size structure. We review previous work demonstrating effects of population structure on baculoviruses of two insects in North America, focusing on heterogeneity in infection risk, which has consequences for both single epizootics and long-term host–pathogen population cycles. Baculoviruses’ simple biology and their insect hosts’ small size means that insect–baculovirus interactions provide experimentally tractable systems for testing models. Our research has combined mechanistic models with data using a combination of statistical model selection, Bayesian statistics, and time-series probes, allowing us to show how host variation affects disease dynamics and pathogen coexistence, and how variation is affected by host-plant resource quality, climate change, and host population size structure. We also discuss how Bayesian mixture models can make it possible to combine multiple sources of data collected across a range of scales.

Keywords

baculoviruseco-evolutionary dynamicsglobal warminghost variationinduced plant defencesLymantria disparmechanistic modelspopulation cyclesSEIR modelSpodoptera frugiperdatransmission model
Type
Chapter
Information
Wildlife Disease Ecology
Linking Theory to Data and Application
 , pp. 225 - 261
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbott, K.C. & Dwyer, G. (2008) Using mechanistic models to understand synchrony in forest insect populations: the North American Gypsy Moth as a case study. American Naturalist, 172, 613–624.CrossRefGoogle ScholarPubMed
Altizer, S., Dobson, A., Hosseini, P., et al. (2006) Seasonality and the dynamics of infectious diseases. Ecology Letters, 9, 467–484.Google Scholar
Altizer, S., Harvell, D. & Friedle, E. (2003) Rapid evolutionary dynamics and disease threats to biodiversity. Trends in Ecology & Evolution, 18, 589–596.CrossRefGoogle Scholar
Anderson, R.M. & May, R.M. (1978) Regulation and stability of host–parasite population interactions: I. Regulatory processes. The Journal of Animal Ecology, 47, 219–247.CrossRefGoogle Scholar
Anderson, R.M. & May, R.M. (1981) The population dynamics of microparasites and their invertebrate hosts. Philosophical Transactions of the Royal Society of London B, 291, 451–524.Google Scholar
Anderson, R.M. & May, R.M. (1991) Infectious Disease of Humans: Dynamics and Control. Oxford: Oxford University Press.Google Scholar
Baldwin, I.T. (1998) Jasmonate-induced responses are costly but benefit plants under attack in native populations. Proceedings of the National Academy of Sciences of the United States of America, 95, 8113–8118.Google Scholar
Barbosa, P. & Krischik, V.A. (1987) Influence of alkaloids on feeding preference of eastern deciduous forest trees by the gypsy moth Lymantria dispar. American Naturalist, 130, 53–69.Google Scholar
Bjørnstad, O.N., Robinet, C. & Liebhold, A.M. (2010) Geographic variation in North American gypsy moth cycles: subharmonics, generalist predators, and spatial coupling. Ecology, 91, 106–118.Google Scholar
Bonsall, M.B., Sait, S.M. & Hails, R.S. (2005) Invasion and dynamics of covert infection strategies in structured insect–pathogen populations. Journal of Animal Ecology, 74, 464–474.Google Scholar
Boots, M., Greenman, J., Ross, D., et al. (2003) The population dynamical implications of covert infections in host–microparasite interactions. Journal of Animal Ecology, 72, 1064–1072.Google Scholar
Burden, J.P., Nixon, C.P., Hodgkinson, A.E., et al. (2003) Covert infections as a mechanism for long-term persistence of baculoviruses. Ecology Letters, 6, 524–531.Google Scholar
Burnham, K. & Anderson, D. (2002) Model Selection and Multimodal Inference: A Practical Information-theoretic Approach. New York, NY: Springer.Google Scholar
Chamberlin, T.C. (1897) Studies for students: the method of multiple working hypotheses. The Journal of Geology, 5, 837–848.Google Scholar
Clark, J.S. (2007) Models for Ecological Data: An Introduction. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Cory, J.S. & Hoover, K. (2006) Plant-mediated effects in insect–pathogen interactions. Trends in Ecology & Evolution, 21, 278–286.Google Scholar
D’Amico, V., Elkinton, J.S., Podgwaite, J.D., Buonaccorsi, J. & Dwyer, G. (2005) Pathogen clumping: an explanation for non-linear transmission of an insect virus. Ecological Entomology, 30, 383–390.CrossRefGoogle Scholar
Dieckmann, U. (2002) Adapative dynamics of pathogen-host interactions. In: Dieckmann, U., Sigmund, K. & Metz, H. (eds.), Adaptive Dynamics of Infectious Diseases: In Pursuit of Virulence Management (pp. 39–59). Cambridge: Cambridge University Press.Google Scholar
Doane, C.C. & McManus, M.L. (eds.). (1981) The Gypsy Moth: Research Toward Integrated Pest Management. Washington, DC: USDA.Google Scholar
Duffy, M.A., Ochs, J.H., Penczykowski, R.M., et al. (2012) Ecological context influences epidemic size and parasite-driven evolution. Science, 335, 1636–1638.CrossRefGoogle ScholarPubMed
Dwyer, G. (1991) The roles of density, stage, and patchiness in the transmission of an insect virus. Ecology, 72, 559–574.CrossRefGoogle Scholar
Dwyer, G., Dushoff, J., Elkinton, J.S., Burand, J.P. & Levin, S.A. (2002) Variation in susceptibility: lessons from an insect virus. In: Dieckmann, U., Sigmund, K. & Metz, H. (eds.), Adaptive Dynamics of Infectious Diseases: In Pursuit of Virulence Management (pp. 74–84). Cambridge: Cambridge University Press.Google Scholar
Dwyer, G., Dushoff, J., Elkinton, J.S. & Levin, S.A. (2000) Pathogen-driven outbreaks in forest defoliators revisited: building models from experimental data. American Naturalist, 156, 105–120.Google Scholar
Dwyer, G., Dushoff, J. & Yee, S.H. (2004) The combined effects of pathogens and predators on insect outbreaks. Nature, 430, 341–345.Google Scholar
Dwyer, G., Elkinton, J.S. & Buonaccorsi, J.P. (1997) Host heterogeneity in susceptibility and disease dynamics: tests of a mathematical model. American Naturalist, 150, 685–707.Google Scholar
Dwyer, G., Firestone, J. & Stevens, T.E. (2005) Should models of disease dynamics in herbivorous insects include the effects of variability in host-plant foliage quality? American Naturalist, 165, 16–31.CrossRefGoogle ScholarPubMed
Eakin, L., Wang, M. & Dwyer, G. (2015) The effects of the avoidance of infectious hosts on infection risk in an insect–pathogen interaction. American Naturalist, 185, 100–112.Google Scholar
Elderd, B.D. (2013) Developing models of disease transmission: insights from ecological studies of insects and their baculoviruses. PLoS Pathogens, 9, e1003372.Google Scholar
Elderd, B.D. (2019) Bottom-up trait-mediated indirect effects decrease pathogen transmission in a tritrophic system. Ecology, 100, e02551.Google Scholar
Elderd, B.D., Dushoff, J. & Dwyer, G. (2008) Host–pathogen interactions, insect outbreaks, and natural selection for disease resistance. American Naturalist, 172, 829–842.Google Scholar
Elderd, B.D., Dwyer, G. & Dukic, V. (2013) Population-level differences in disease transmission: a Bayesian analysis of multiple smallpox epidemics. Epidemics, 5, 146–156.Google Scholar
Elderd, B.D. & Miller, T.E.X. (2016) Quantifying uncertainty in demographic models: Bayesian methods for Integral Projection Models (IPMs). Ecological Monographs, 86, 125–144.CrossRefGoogle Scholar
Elderd, B.D., Rehill, B.J., Haynes, K.J. & Dwyer, G. (2013) Induced plant defenses, host–pathogen interactions, and forest insect outbreaks. Proceedings of the National Academy of Sciences of the United States of America, 110, 14,978–14,983.Google Scholar
Elderd, B.D. & Reilly, J. (2014) Warmer temperatures increase disease transmission and outbreak intensity in a host–pathogen system. Journal of Animal Ecology, 83, 838–849.Google Scholar
Elkinton, J.S., Healy, W.M., Buonaccorsi, J.P., et al. (1996) Interactions among gypsy moths, white-footed mice, and acorns. Ecology, 77, 2332–2342.CrossRefGoogle Scholar
Elkinton, J. & Liebhold, A. (1990) Population dynamics of gypsy moth in North America. Annual Review of Entomology, 35, 571–596.Google Scholar
Ferrari, M.J., Grais, R.F., Bharti, N., et al. (2008) The dynamics of measles in sub-Saharan Africa. Nature, 451, 679–684.Google Scholar
Fleming-Davies, A.E., Dukic, V., Andreasen, V. & Dwyer, G. (2015) Effects of host heterogeneity on pathogen diversity and evolution. Ecology Letters, 18, 1252–1261.CrossRefGoogle ScholarPubMed
Fleming-Davies, A.E. & Dwyer, G. (2015) Phenotypic variation in overwinter environmental transmission of a baculovirus and the cost of virulence. American Naturalist, 186, 797–806.Google Scholar
Fox, L.R. (1975) Cannibalism in natural-populations. Annual Review of Ecology and Systematics, 6, 87–106.Google Scholar
Fuller, E., Elderd, B.D. & Dwyer, G. (2012) Pathogen persistence in the environment and insect–baculovirus interactions: disease-density thresholds, epidemic burnout, and insect outbreaks. American Naturalist, 179, E70–E96.Google Scholar
Fuxa, J.R. (1982) Prevalence of viral infections in populations of fall armyworm, Spodoptera frugiperda, in Southeastern Louisiana. Environmental Entomology, 11, 239–242.CrossRefGoogle Scholar
Fuxa, J.R. (1987) Spodoptera frugiperda susceptibility to nuclear polyhedrosis-virus isolates with reference to insect migration. Environmental Entomology, 16, 218–223.Google Scholar
Fuxa, J.R. & Geaghan, J.P. (1983) Multiple-regression analysis of factors affecting prevalence of nuclear polyhedrosis virus in Spodoptera frugiperda (Lepidoptera, Noctuidae) populations. Environmental Entomology, 12, 311–316.CrossRefGoogle Scholar
Fuxa, J., Mitchell, F. & Richter, A. (1988) Resistance of Spodoptera frugiperda (Lepidoptera: Noctuidae) to a nuclear polyhedrosis virus in the field and laboratory. BioControl, 33, 55–63.Google Scholar
Gelman, A., Carlin, J.B., Stern, H.S. & Rubin, D.B. (2014) Bayesian Data Analysis. Boca Raton, FL: Taylor & Francis.Google Scholar
Godfree, R., Robertson, B., Bolger, T., Carnegie, M. & Young, A. (2011) An improved hexagon open-top chamber system for stable diurnal and nocturnal warming and atmospheric carbon dioxide enrichment. Global Change Biology, 17, 439–451.Google Scholar
Grzywacz, D., Mushobozi, W.L., Parnell, M., Jolliffe, F. & Wilson, K. (2008) Evaluation of Spodoptera exempta nucleopolyhedrovirus (SpexNPV) for the field control of African armyworm (Spodoptera exempta) in Tanzania. Crop Protection, 27, 17–24.CrossRefGoogle Scholar
Hajek, A.E. (1999) Pathology and epizootiology of Entomophaga maimaiga infections in forest Lepidoptera. Microbiology and Molecular Biology Reviews, 63, 814–835.Google Scholar
Hajek, A.E., Tobin, P.C. & Haynes, K.J. (2015) Replacement of a dominant viral pathogen by a fungal pathogen does not alter the collapse of a regional forest insect outbreak. Oecologia, 177, 785–797.CrossRefGoogle Scholar
Hall, S.R., Simonis, J.L., Nisbet, R.M., Tessier, A.J. & Caceres, C.E. (2009) Resource ecology of virulence in a planktonic host–parasite system: an explanation using dynamic energy budgets. American Naturalist, 174, 149–162.Google Scholar
Harvell, C.D., Mitchell, C.E., Ward, J.R., et al. (2002) Ecology – Climate warming and disease risks for terrestrial and marine biota. Science, 296, 2158–2162.Google Scholar
Hassell, M.P., May, R.M., Pacala, S.W. & Chesson, P.L. (1991) The persistence of host–parasitoid associations in patchy environments. I. A general criterion. The American Naturalist, 138, 568–583.Google Scholar
Hethcote, H.W. (1976) Qualitative analyses of communicable disease models. Mathematical Biosciences, 28, 335–356.Google Scholar
Hilborn, R. & Mangel, M. (1997) The Ecological Detective: Confronting Models with Data. Princeton, NJ: Princeton University Press.Google Scholar
Hinds, W. & Dew, J. (1915) The grass worm or fall army worm. Bulletin no. 186, Alabama Agricultural Experiment Station.Google Scholar
Hobbs, N.T., Geremia, C., Treanor, J., et al. (2015) State-space modeling to support management of brucellosis in the Yellowstone bison population. Ecological Monographs, 85, 525–556.Google Scholar
Hobbs, N.T. & Hooten, M.B. (2015) Bayesian Models: A Statistical Primer for Ecologists. Princeton, NJ: Princeton University Press.Google Scholar
Hooten, M.B. & Hobbs, N.T. (2015) A guide to Bayesian model selection for ecologists. Ecological Monographs, 85, 3–28.Google Scholar
Hoover, K., Grove, M., Gardner, M., et al. (2011) A gene for an extended phenotype. Science, 333, 1401–1401.Google Scholar
Hudson, A.I., Fleming-Davies, A.E., Páez, D.J. & Dwyer, G. (2016) Genotype-by-genotype interactions between an insect and its pathogen. Journal of Evolutionary Biology, 29, 2480–2490.Google Scholar
Hudson, P.J., Dobson, A.P. & Newborn, D. (1998) Prevention of population cycles by parasite removal. Science, 282, 2256–2258.Google Scholar
Hudson, P.J., Rizzoli, A., Grenfell, B.T., Heesterbeek, H. & Dobson, A.P. (2002) The Ecology of Wildlife Diseases. Oxford: Oxford University Press.Google Scholar
Hunter, M.D. & Schultz, J.C. (1993) Induced plant defenses breached? Phytochemical induction protects an herbivore from disease. Oecologia, 94, 195–203.Google Scholar
Hunter, M. (2016) The Phytochemical Landscape: Linking Trophic Interactions and Nutrient Dynamics. Princeton, NJ:: Princeton University Press.Google Scholar
Hunter-Fujita, F.R., Entwistle, P., Evans, H., et al. (1998) Insect Viruses and Pest Management. Chichester: John Wiley & Sons Ltd.Google Scholar
Ionides, E., Bretó, C. & King, A. (2006) Inference for nonlinear dynamical systems. Proceedings of the National Academy of Sciences of the United States of America, 103, 18,438–18,443.Google Scholar
Johnson, D.M., Liebhold, A.M. & Bjørnstad, O.N. (2006) Geographical variation in the periodicity of gypsy moth outbreaks. Ecography, 29, 367–374.Google Scholar
Johnson, D.M., Liebhold, A.M., Bjørnstad, O.N. & McManus, M.L. (2005) Circumpolar variation in periodicity and synchrony among gypsy moth populations. Journal of Animal Ecology, 74, 882–892.Google Scholar
Johnson, P.T., De Roode, J.C. & Fenton, A. (2015) Why infectious disease research needs community ecology. Science, 349, 1259504.Google Scholar
Karban, R. & Baldwin, I.T. (1997) Induced Responses to Herbivory. Chicago, IL: University of Chicago Press.Google Scholar
Keeling, M.J. & Rohani, P. (2008) Modeling Infectious Diseases in Humans and Animals. Princeton, NJ: Princeton University Press.Google Scholar
Kendall, B.E., Briggs, C.J., Murdoch, W.W., et al. (1999) Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches. Ecology, 80, 1789–1805.Google Scholar
Kennedy, D.A., Dukic, V. & Dwyer, G. (2014) Pathogen growth in insect hosts: inferring the importance of different mechanisms using stochastic models and response-time data. American Naturalist, 184, 407–423.Google Scholar
Kéry, M. (2010) Introduction to WinBUGS for Ecologists. Boston, MA: Academic Press.Google Scholar
King, A.A., Ionides, E.L., Pascual, M. & Bouma, M.J. (2008) Inapparent infections and cholera dynamics. Nature, 454, 877–880.Google Scholar
Konishi, S. & Kitagawa, G. (2008) Information Criteria and Statistical Modeling. New York, NY: Springer Science & Business Media.Google Scholar
Kot, M. (2001) Elements of Mathematical Ecology. Cambridge: Cambridge University Press.Google Scholar
LaDeau, S.L., Glass, G.E., Hobbs, N.T., Latimer, A. & Ostfeld, R.S. (2011) Data–model fusion to better understand emerging pathogens and improve infectious disease forecasting. Ecological Applications, 21, 1443–1460.Google Scholar
Lee, K.P., Cory, J.S., Wilson, K., Raubenheimer, D. & Simpson, S.J. (2006) Flexible diet choice offsets protein costs of pathogen resistance in a caterpillar. Proceedings of the Royal Society of London B, 273, 823–829.Google Scholar
Levin, S.A. (1992) The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology, 73, 1943–1967.Google Scholar
Link, W.A. & Barker, R.J. (2010) Bayesian Inference with Ecological Applications. London: Academic Press.Google Scholar
May, R.M. & Anderson, R.M. (1978) Regulation and stability of host–parasite population interactions: II. Destabilizing processes. Journal of Animal Ecology, 47, 249–267.CrossRefGoogle Scholar
May, R.M. & Anderson, R.M. (1979) Population biology of infectious diseases: Part II. Nature, 280, 455–461.Google Scholar
McCallum, H. (2016) Models for managing wildlife disease. Parasitology, 143, 805–820.Google Scholar
McCallum, H., Fenton, A., Hudson, P., et al. (2017) Breaking beta: deconstructing the parasite transmission function. Philosophical Transactions of the Royal Society of London B, 372, 20160084.Google Scholar
Miller, L.K. (2013) The Baculoviruses. New York, NY: Springer Science & Business Media.Google Scholar
Mitchell, E., McNeil, J., Westbrook, J., et al. (1991) Seasonal periodicity of fall armyworm (Lepidoptera: Noctuidae) in the Caribbean basin and northward to Canada. Journal of Entomological Science, 26, 39–50.Google Scholar
Moreau, G. & Lucarotti, C.J. (2007) A brief review of the past use of baculoviruses for the management of eruptive forest defoliators and recent developments on a sawfly virus in Canada. The Forestry Chronicle, 83, 105–112.Google Scholar
Myers, J.H. (1988) Can a general hypothesis explain population cycles of forest Lepidoptera? Advances in Ecological Research, 18, 179–242.Google Scholar
Myers, J.H. & Cory, J.S. (2016) Ecology and evolution of pathogens in natural populations of Lepidoptera. Evolutionary Applications, 9, 231–247.Google Scholar
New, L.F., Matthiopoulos, J., Redpath, S. & Buckland, S.T. (2009) Fitting models of multiple hypotheses to partial population data: investigating the causes of cycles in red grouse. American Naturalist, 174, 399–412.Google Scholar
Nykänen, H. & Koricheva, J. (2004) Damage-induced changes in woody plants and their effects on insect herbivore performance: a meta-analysis. Oikos, 104, 247–268.Google Scholar
O’Neill, P.D. (2002) A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo method. Mathematical Biosciences, 180, 103–114.Google Scholar
Páez, D.J., Dukic, V., Dushoff, J., Fleming-Davies, A.E. & Dwyer, G. (2017) Eco-evolutionary theory and insect outbreaks. American Naturalist, 189, 616–629.CrossRefGoogle ScholarPubMed
Parker, B.J., Elderd, B.D. & Dwyer, G. (2010) Host behaviour and exposure risk in an insect–pathogen interaction. Journal of Animal Ecology, 79, 863–870.Google Scholar
Pascual, M. & Bouma, M.J. (2009) Do rising temperatures matter? Ecology, 90, 906–912.Google Scholar
Pepin, K.M., Kay, S.L., Golas, B.D., et al. (2017) Inferring infection hazard in wildlife populations by linking data across individual and population scales. Ecology Letters, 20, 275–292.Google Scholar
Pfennig, D.W. (2000) Effect of predator–prey phylogenetic similarity on the fitness consequences of predation: a trade-off between nutrition and disease? American Naturalist, 155, 335–345.Google Scholar
Pitre, H.N. & Hogg, D.B. (1983) Development of the fall armyworm (Lepidoptera, Noctuidae) on cotton, soybean and corn. Journal of the Georgia Entomological Society, 18, 182–187.Google Scholar
Polis, G.A. (1981) The evolution and dynamics of intraspecific predation. Annual Review of Ecology and Systematics, 12, 225–251.Google Scholar
Redman, E.M., Wilson, K. & Cory, J.S. (2016) Trade-offs and mixed infections in an obligate-killing insect pathogen. Journal of Animal Ecology, 85, 1200–1209.Google Scholar
Reeson, A.F., Wilson, K., Cory, J.S., et al. (2000) Effects of phenotypic plasticity on pathogen transmission in the field in a Lepidoptera–NPV system. Oecologia, 124, 373–380.Google Scholar
Reeson, A.F., Wilson, K., Gunn, A., Hails, R.S. & Goulson, D. (1998) Baculovirus resistance in the noctuid Spodoptera exempta is phenotypically plastic and responds to population density. Proceedings of the Royal Society of London B, 265, 1787–1791.Google Scholar
Reilly, J.R. & Elderd, B.D. (2014) Effects of biological control on long-term population dynamics: identifying unexpected outcomes. Journal of Applied Ecology, 51, 90–101.Google Scholar
Richardson, M.L., Mitchell, R.F., Reagel, P.F. & Hanks, L.M. (2010) Causes and consequences of cannibalism in noncarnivorous insects. Annual Review of Entomology, 55, 39–53.CrossRefGoogle ScholarPubMed
Richter, A.R., Fuxa, J.R. & Abdelfattah, M. (1987) Effect of host plant on the susceptibility of Spodoptera frugiperda (Lepidoptera, Noctuidae) to a nuclear polyhedrosis virus. Environmental Entomology, 16, 1004–1006.Google Scholar
Rohrmann, G.F. (2014) Baculovirus nucleocapsid aggregation (MNPV vs SNPV): an evolutionary strategy, or a product of replication conditions? Virus Genes, 49, 351–357.Google Scholar
de Roode, J.C., Pedersen, A.B., Hunter, M.D. & Altizer, S. (2008) Host plant species affects virulence in monarch butterfly parasites. Journal of Animal Ecology, 77, 120–126.Google Scholar
Rudolf, V.H. (2007) Consequences of stage-structured predators: cannibalism, behavioral effects, and trophic cascades. Ecology, 88, 2991–3003.CrossRefGoogle ScholarPubMed
Rudolf, V.H.W., Kamo, M. & Boots, M. (2010) Cannibals in space: the coevolution of cannibalism and dispersal in spatially structured populations. American Naturalist, 175, 513–524.Google Scholar
Schultz, J.C. & Baldwin, I.T. (1982) Oak leaf quality declines in response to defoliation by gypsy moth larvae. Science, 217, 149–151.Google Scholar
Shikano, I., Shumaker, K.L., Peiffer, M., Felton, G.W. & Hoover, K. (2017) Plant-mediated effects on an insect–pathogen interaction vary with intraspecific genetic variation in plant defences. Oecologia, 183, 1121–1134.Google Scholar
Spiegelhalter, D.J., Best, N.G., Carlin, B.R. & van der Linde, A. (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B Statistical Methodology, 64, 583–616.Google Scholar
Thompson, J. (2005) The Geographic Mosaic of Coevolution. Chicago, IL: University of Chicago Press.Google Scholar
Underwood, N., Rausher, M. & Cook, W. (2002) Bioassay versus chemical assay: measuring the impact of induced and constitutive resistance on herbivores in the field. Oecologia, 131, 211–219.Google Scholar
Valicente, F.H., Tuelher, E.S., Pena, R.C., Andreazza, R. & Guimaraes, M.R.F. (2013) Cannibalism and virus production in Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae) larvae fed with two leaf substrates inoculated with Baculovirus spodoptera. Neotropical Entomology, 42, 191–199.Google Scholar
Van Allen, B.G., Dillemuth, F.P., Flick, A.J., et al. (2017) Cannibalism and infectious disease: friend or foe? American Naturalist, 120, 299–312.Google Scholar
Verity, R., Stevenson, M.D., Rossmo, D.K., Nichols, R.A. & Le Comber, S.C. (2014) Spatial targeting of infectious disease control: identifying multiple, unknown sources. Methods in Ecology and Evolution, 5, 647–655.Google Scholar
Vilaplana, L., Wilson, K., Redman, E.M. & Cory, J.S. (2010) Pathogen persistence in migratory insects: high levels of vertically-transmitted virus infection in field populations of the African armyworm. Evolutionary Ecology, 24, 147–160.Google Scholar
Wang, L., Hui, C., Sandhu, H.S., Li, Z. & Zhao, Z. (2015) Population dynamics and associated factors of cereal aphids and armyworms under global change. Scientific Reports, 5, 18801.Google Scholar
Watanabe, S. (2013) A widely applicable Bayesian information criterion. Journal of Machine Learning Research, 14, 867–897.Google Scholar
Werner, E. & Peacor, S. (2003) A review of trait-mediated indirect interactions in ecological communities. Ecology, 84, 1083–1100.Google Scholar
Wild, S. (2017) African countries mobilize to battle invasive caterpillar. Nature, 543, 13.Google Scholar
Williams, D.W. & Liebhold, A.M. (1995) Herbivorous insects and global change: potential changes in the spatial distribution of forest defoliator outbreaks. Journal of Biogeography, 22, 665–671.Google Scholar
Williams, T. & Hernádez, O. (2006) Costs of cannibalism in the presence of an iridovirus pathogen of Spodoptera frugiperda. Ecological Entomology, 31, 106–113.Google Scholar
Wilson, K., Bjørnstad, O., Dobson, A., et al. (2002) Heterogeneities in macroparasite infections: Patterns and processes. In: Hudson, P., Rizzoli, A., Grenfell, B., Heesterbeek, H. & Dobson, A.P. (eds.), The Ecology of Wildlife Diseases (pp. 6–44). Oxford: Oxford University Press.Google Scholar
Wold, E.N. & Marquis, R.J. (1997) Induced defense in white oak: effects on herbivores and consequences for the plant. Ecology, 78, 1356–1369.Google Scholar
Woods, S. & Elkinton, J. (1987) Biomodal patterns of mortality from nuclear polyhedrosis virus in gypsy moth (Lymantria dispar) populations. Journal of Invertebrate Pathology,50,151–157.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×