Host–pathogen dynamics are influenced by many factors that vary locally, but models of disease rarely consider dynamics across spatially heterogeneous environments. In addition, theory predicts that dispersal will influence host–pathogen dynamics of populations that are linked, although this has not been examined empirically in natural systems. We examined the spatial dynamics of a patchy population of tiger moths and its baculovirus pathogen, in which habitat type and weather influence dynamics. Theoretical models of host–baculovirus dynamics predict that such variation in dynamics between habitat types could be driven by a range of factors, of which we predict two are likely to be operating in this system: (1) differences in the environmental persistence of pathogens or (2) differences in host intrinsic rates of increase. We used time series models and monitored infection rates of hosts to characterize population and disease dynamics and distinguish between these possibilities. We also examined the role of host dispersal (connectivity) and weather as important contributors to dynamics, using time series models and experiments. We found that the population growth rate was higher, delayed density dependence was weaker, and long‐period oscillations had lower amplitudes in high‐quality habitat patches. The infection rate was higher on average in high‐quality habitat, and this was likely to have been driven by higher mean population densities and no differences in pathogen persistence in different habitats (delayed density dependence). Time series modeling and experiments also showed an interactive effect of temperature and precipitation on moth population growth rates (likely caused by variation in host plant quality and quantity), and an effect of connectivity. Our results showed that spatial heterogeneity, connectivity, climate, and their interactions were important in driving host–baculovirus dynamics. In particular, our study found that connected patches and spatial heterogeneity generated differences in dynamics that only partially aligned with theoretical predictions.
- Award ID(s):
- 2001213
- NSF-PAR ID:
- 10309631
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-021-90407-z
