PNAS – Proceedings of the National Academy of Sciences of the United States
[Accessed 30 Jun 2018]
Lauren A. White, James D. Forester, and Meggan E. Craft
PNAS June 25, 2018. 201801383; published ahead of print June 25, 2018
Understanding how emerging infectious and zoonotic diseases spread through space and time is critical for predicting outbreaks and designing interventions; disease models are important tools for realizing these goals. Currently, humans are altering the environment in unprecedented ways through urbanization, habitat fragmentation, and climate change. However, the consequences of increasingly heterogeneous landscapes on pathogen transmission and persistence remain unclear. By synthesizing mathematical modeling and movement ecology approaches, we examined how wildlife movement patterns interact with broad-scale landscape structure to affect population-level disease dynamics. We found that habitat fragmentation could counterintuitively promote disease outbreaks but that, for higher wildlife densities and longer infectious periods, small differences in how hosts navigated their environments could dramatically alter observed disease dynamics.
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.