Plant disease in natural systems has historically often been assumed to be in equilibrium such that it had no substantial influence on plant species fitness.  However, three major anthropogenic factors currently have the potential to magnify the impact of disease on plant communities.  (1) Land use change often results in natural plant communities surrounded by agricultural systems that may share common pathogens.  (2) Accelerated transportation networks and exchange of plant materials have introduced new pathogens and vectors to most plant communities of the world.  (3) Climate change shifts epidemic conditions, producing new host-pathogen-vector assemblages and altering general epidemic parameters.

Results/Conclusions

(1) Land use change can produce scenarios analogous to zoonotic disease, where pathogens move between plants actively managed by humans and more natural plant communities.  For example, we have found that isolates of the generalist pathogen Macrophomina phaseolina from tallgrass prairie plants and from agricultural species such as soybean are often closely related, suggesting movement between these systems.  Models of the effects of landscape structure indicate that shifts in host patch patterns can have surprisingly different effects on epidemics depending on the scale being considered.  (2) Land use change can also influence networks of pathogen movement.  For example, the newly introduced soybean rust moves in the US through networks of soybean and the invasive kudzu in an annual migration from south to north, infecting a number of other species which would not otherwise be present in large enough numbers to generate the huge quantity of inoculum.  We have developed network models for this invasive species, and this approach holds great promise for identifying highly connected regions that are particularly important for sampling such invasive species.  Network models can also be used to formalize ideas about how plant diversity provides disease regulation as an ecosystem service.  (3)  The effects of climate change on disease impacts may be the most difficult to predict, because of the great potential for nonlinear relationships, important interactions, and feedback loops.  For example, we have shown that increases in season length are associated with decreases in the effects of host diversity for reducing disease pressure.  Changes in temperature and precipitation can also modify the effects of disease resistance genes.  New tools in ecological genomics will support understanding of what limitations may exist for plants’ abilities to adapt to new combinations of biotic and abiotic stresses.