Disease models have provided conflicting evidence as to whether spatial heterogeneity promotes or impedes pathogen persistence. Moreover, there has been limited theoretical investigation into how animal movement behavior interacts with the spatial organization of resources (e.g., clustered, random, uniform) across a landscape to affect infectious disease dynamics. Importantly, spatial heterogeneity of resources can sometimes lead to nonlinear or counterintuitive outcomes depending on the host and pathogen system. There is a clear need to develop a general theoretical framework that could be used to create testable predictions for specific host–pathogen systems. Here, we develop an individual-based model integrated with movement ecology approaches to investigate how host movement behaviors interact with landscape heterogeneity (in the form of various levels of resource abundance and clustering) to affect pathogen dynamics. For most of the parameter space, our results support the counterintuitive idea that fragmentation promotes pathogen persistence, but this finding was largely dependent on perceptual range of the host, conspecific density, and recovery rate. For simulations with high conspecific density, slower recovery rates, and larger perceptual ranges, more complex disease dynamics emerged, and the most fragmented landscapes were not necessarily the most conducive to outbreaks or pathogen persistence. These results point to the importance of interactions between landscape structure, individual movement behavior, and pathogen transmission for predicting and understanding disease dynamics.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Jul 10 2018|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS. We acknowledge the Minnesota Supercomputing Institute at the University of Minnesota for providing resources that contributed to the research results reported within this paper (https://www.msi.umn. edu/). L.A.W. was funded by National Science Foundation Grants GRFP-00039202 and DEB-1701069 and the University of Minnesota Informatics Institute. M.E.C. was funded by National Science Foundation Grants DEB-1413925 and DEB-1654609, the University of Minnesota’s Office of the Vice President for Research and Academic Health Center Seed Grant.
- Disease model
- Landscape fragmentation
- Perceptual range
- Resource selection function
- Spatial heterogeneity