• coevolution;
  • epidemiology;
  • life history;
  • resistance;
  • spatial;
  • virulence


Infectious disease is a major causal factor in the demography of human, plant and animal populations. While it is generally accepted in medical, veterinary and agricultural contexts that variation in host resistance and pathogen virulence and aggressiveness is of central importance to understanding patterns of infection, there has been remarkably little effort to directly investigate causal links between population genetic structure and disease dynamics, and even less work on factors influencing host–pathogen coevolution. The lack of empirical evidence is particularly surprising, given the potential for such variation to not only affect disease dynamics and prevalence, but also when or where new diseases or pathotypes emerge. Increasingly, this lack of knowledge has led to calls for an integrated approach to disease management, incorporating both ecological and evolutionary processes. Here, we argue that plant pathogens occurring in agro-ecosystems represent one clear example where the application of evolutionary principles to disease management would be of great benefit, as well as providing model systems for advancing our ability to generalize about the long-term coevolutionary dynamics of host–pathogen systems. We suggest that this is particularly the case given that agro-ecological host–pathogen interactions represent a diversity of situations ranging from those that only involve agricultural crops through to those that also include weedy crop relatives or even unrelated native plant communities. We begin by examining some of the criteria that are important in determining involvement in agricultural pathogen evolution by noncrop plants. Throughout we use empirical examples to illustrate the fact that different processes may dominate in different systems, and suggest that consideration of life history and spatial structure are central to understanding dynamics and direction of the interaction. We then discuss the implications that such interactions have for disease management in agro-ecosystems and how we can influence those outcomes. Finally, we identify several major gaps where future research could increase our ability to utilize evolutionary principles in managing disease in agro-ecosystems.