Spatiotemporal Structure of Host‐Pathogen Interactions in a Metapopulation

The ecological and evolutionary dynamics of species are influenced by spatiotemporal variation in population size. Unfortunately, we are usually limited in our ability to investigate the numerical dynamics of natural populations across large spatial scales and over long periods of time. Here we combine mechanistic and statistical approaches to reconstruct continuous‐time infection dynamics of an obligate fungal pathogen on the basis of discrete‐time occurrence data. The pathogen, Podosphaera plantaginis, infects its host plant, Plantago lanceolata, in a metapopulation setting where the presence of the pathogen has been recorded annually for 6 years in ∼4,000 host populations across an area of 50 km × 70 km in Finland. The dynamics are driven by strong seasonality, with a high extinction rate during winter and epidemic expansion in summer for local pathogen populations. We are able to identify with our model the regions in the study area where overwintering has been most successful. These overwintering sites represent foci that initiate local epidemics during the growing season. There is striking heterogeneity at the regional scale in both the overwintering success of the pathogen and the encounter intensity between the host and the pathogen. Such heterogeneity has profound implications for the coevolutionary dynamics of the interaction.

[1]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[2]  B. Manly Randomization, Bootstrap and Monte Carlo Methods in Biology , 2018 .

[3]  S. Soubeyrand,et al.  Inference with a contrast-based posterior distribution and application in spatial statistics , 2009 .

[4]  J. Gamarra,et al.  Metapopulation Ecology , 2007 .

[5]  A. Sasaki,et al.  ‘Small worlds’ and the evolution of virulence: infection occurs locally and at a distance , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[6]  Anna‐Liisa Laine Pathogen fitness components and genotypes differ in their sensitivity to nutrient and temperature variation in a wild plant–pathogen association , 2007, Journal of evolutionary biology.

[7]  R M Fewster,et al.  A spatiotemporal stochastic process model for species spread. , 2003, Biometrics.

[8]  P. Sprent,et al.  Statistical Analysis of Circular Data. , 1994 .

[9]  Matthew James Keeling Spatial Models of Interacting Populations , 2009 .

[10]  S. Soubeyrand,et al.  Anisotropy, in density and in distance, of the dispersal of yellow rust of wheat: experiments in large field plots and estimation. , 2007, Phytopathology.

[11]  Oliver Penrose,et al.  Modern ergodic theory , 1973 .

[12]  Christopher K. Wikle,et al.  Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes , 2003 .

[13]  J. Burdon,et al.  Evolution of gene‐for‐gene systems in metapopulations: the effect of spatial scale of host and pathogen dispersal , 2002 .

[14]  J. Thompson,et al.  The Coevolutionary Process , 1994 .

[15]  H. Godfray,et al.  Host-parasitoid dynamics , 1998 .

[16]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[17]  Katriona Shea,et al.  An integrated approach to management in epidemiology and pest control , 2000 .

[18]  Volker Grimm,et al.  Using pattern-oriented modeling for revealing hidden information: a key for reconciling ecological theory and application , 2003 .

[19]  Christl A. Donnelly,et al.  The Foot-and-Mouth Epidemic in Great Britain: Pattern of Spread and Impact of Interventions , 2001, Science.

[20]  A. Penttinen,et al.  Mechanical-Statistical Modeling in Ecology: From Outbreak Detections to Pest Dynamics , 2009, Bulletin of mathematical biology.

[21]  C. Benkman The Selection Mosaic and Diversifying Coevolution between Crossbills and Lodgepole Pine , 1999, The American Naturalist.

[22]  D. Stoyan,et al.  Statistical Analysis and Modelling of Spatial Point Patterns , 2008 .

[23]  I. Hanski,et al.  Structure and dynamics of Melitaea cinxia metapopulations , 2004 .

[24]  P. David,et al.  Metapopulation Dynamics and Biological Invasions: A Spatially Explicit Model Applied to a Freshwater Snail , 2006, The American Naturalist.

[25]  M. Keeling,et al.  The effects of local spatial structure on epidemiological invasions , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[26]  I. Hanski,et al.  Large‐scale spatial dynamics of a specialist plant pathogen in a fragmented landscape , 2006 .

[27]  S. T. Bucklanda,et al.  State-space models for the dynamics of wild animal populations , 2003 .

[28]  D. Stoyan,et al.  Stochastic Geometry and Its Applications , 1989 .

[29]  J. Thompson,et al.  Geographic structure and dynamics of coevolutionary selection , 2002, Nature.

[30]  Uta Berger,et al.  Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology , 2005, Science.

[31]  R. Gomulkiewicz,et al.  Dos and don'ts of testing the geographic mosaic theory of coevolution , 2007, Heredity.

[32]  O. Ovaskainen,et al.  Inferring evolutionary signals from ecological data in a plant-pathogen metapopulation. , 2006, Ecology.

[33]  W. Hamilton Sex versus non-sex versus parasite , 1980 .

[34]  Robert M. May,et al.  The spatial dynamics of host-parasitoid systems , 1992 .

[35]  Nicholas I. Fisher,et al.  Statistical Analysis of Circular Data , 1993 .

[36]  Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times , 2007 .

[37]  Robert D Holt,et al.  Spatial Heterogeneity, Indirect Interactions, and the Coexistence of Prey Species , 1984, The American Naturalist.

[38]  L. Held,et al.  Modelling the spread in space and time of an airborne plant disease , 2008 .

[39]  J. Thompson,et al.  Specific Hypotheses on the Geographic Mosaic of Coevolution , 1999, The American Naturalist.

[40]  D. Balding,et al.  Approximate Bayesian computation in population genetics. , 2002, Genetics.

[41]  J. Burdon,et al.  Spatial and Temporal Patterns in Coevolving Plant and Pathogen Associations , 1999, The American Naturalist.

[42]  Timothy H Keitt,et al.  Spatial and Temporal Heterogeneity Explain Disease Dynamics in a Spatially Explicit Network Model , 2008, The American Naturalist.

[43]  T. Mattfeldt Stochastic Geometry and Its Applications , 1996 .

[44]  Jürgen Symanzik,et al.  Statistical Analysis of Spatial Point Patterns , 2005, Technometrics.

[45]  G. C. Wei,et al.  A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms , 1990 .

[46]  Éric Parent,et al.  A Bayesian state-space modelling framework for fitting a salmon stage-structured population dynamic model to multiple time series of field data , 2004 .

[47]  S. Horn,et al.  Goodness-of-fit tests for discrete data: a review and an application to a health impairment scale. , 1977, Biometrics.

[48]  B. Bohannan,et al.  Gene Flow Reverses an Adaptive Cline in a Coevolving Host‐Parasitoid Interaction , 2007, The American Naturalist.

[49]  G. Gibson Markov Chain Monte Carlo Methods for Fitting Spatiotemporal Stochastic Models in Plant Epidemiology , 1997 .

[50]  B. Bohannan,et al.  Adaptation varies through space and time in a coevolving host–parasitoid interaction , 2004, Nature.

[51]  L. Mark Berliner,et al.  Physical‐statistical modeling in geophysics , 2003 .

[52]  P. Ehrlich,et al.  On the wings of checkerspots : a model system for population biology , 2004 .

[53]  S. Cornell,et al.  Dynamics of the 2001 UK Foot and Mouth Epidemic: Stochastic Dispersal in a Heterogeneous Landscape , 2001, Science.

[54]  A. Buckling,et al.  The effect of migration on local adaptation in a coevolving host–parasite system , 2005, Nature.