Pollinator declines can leave communities less diverse and potentially at increased risk to infectious diseases. Species-rich plant and bee communities have high species turnover, making the study of disease dynamics challenging. To address how temporal dynamics shape parasite prevalence in plant and bee communities, we screened >5,000 bees and flowers through an entire growing season for five common bee microparasites ( Nosema ceranae , N. bombi , Crithidia bombi , C. expoeki and neogregarines). Over 110 bee species and 89 flower species were screened, revealing 42% of bee species (12.2% individual bees) and 70% of flower species (8.7% individual flowers) had at least one parasite in or on them, respectively. Some common flowers (e.g., Lychnis flos-cuculi ) harboured multiple parasite species, whilst others (e.g., Lythrum salicaria ) had few. Significant temporal variation of parasite prevalence in bees was linked to bee diversity, bee and flower abundance, and community composition. Specifically, we found that bee communities had the highest prevalence late in the season, when social bees ( Bombus spp. and Apis mellifera ) were dominant and bee diversity was lowest. Conversely, prevalence on flowers was lowest late in the season when floral abundance was the highest. Thus, turnover in the bee community impacted community-wide prevalence, and turnover in the plant community impacted when parasite transmission was likely to occur at flowers. These results imply that efforts to improve bee health will benefit from promoting high floral numbers to reduce transmission risk, maintaining bee diversity to dilute parasites, and monitoring the abundance of dominant competent hosts.

Recent tragedies such as Hurricane Katrina, 9/11, and the 2008 Sichuan Earthquake have revealed a need for methods to evaluate and plan for the impact of extreme events on critical infrastructure. In particular, awareness has been raised of the threat that a major disruption will lead to cascading failures that cross boundaries between interdependent infrastructure sectors, greatly magnifying human and economic impacts. To assist in planning for such extreme events, researchers are developing modeling tools to aid in making decisions about how best to protect critical infrastructures. We present some of the capabilities of this modeling approach as well as some of the challenges faced in developing such applications based on our experience with the Critical Infrastructure Protection Decision Support System (CIPDSS) model, developed for use by the Department of Homeland Security. A set of disruptions to road and telecommunication infrastructures is implemented in CIPDSS and the modeled disruptions to the original infrastructure as well as cascading effects on other infrastructure sectors are discussed. These simulations provide insights into the potential of this approach. Copyright 2009 by The Policy Studies Organization.

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