97th ESA Annual Meeting (August 5 -- 10, 2012)

COS 131-2 - Disease ecology: New conceptual models to facilitate prediction

Thursday, August 9, 2012: 8:20 AM
D139, Oregon Convention Center
Felicia Keesing, Program in Biology, Bard College, Annandale-On-Hudson, NY and Richard S. Ostfeld, Cary Institute of Ecosystem Studies, Millbrook, NY
Background/Question/Methods

Research on the ecology of infectious diseases has the potential to provide valuable insights into how best to predict, prevent, and manage outbreaks of pests and pathogens. However, despite some successes, disease ecologists have a fairly limited ability to provide useful information in a timely manner. This is largely because we are rarely able to anticipate the ecology of disease systems without extensive study. Recently, the dilution effect – a phenomenon in which high biodiversity reduces the transmission rate of pathogens – has emerged as a conceptual model that allows researchers to predict the behavior of some disease systems. Because of this, the dilution effect has received considerable attention from scientists, the media, and policy-makers.

Results/Conclusions

In our talk, we will describe at least three new conceptual models that, like the dilution effect, emerge from the latest research, but that, unlike the dilution effect, are not receiving the attention they deserve. The first model – the social stress amplification hypothesis – suggests that the shedding rate of pathogens increases when the host is experiencing stress. This result has many implications for the emergence and cross-species transmission of pathogens. The second model – the social breakdown hypothesis – suggests that activities or events that disrupt bonds in social species are likely to result in greater movement of members of that species, and thus greater spatial spread of the pathogens they carry. The third model – the pulsed resources hypothesis – provides a framework for anticipating increases in the abundance of hosts or vectors far in advance, facilitating prediction of disease risk, especially for systems with density-dependent transmission. We will describe these conceptual models in detail, providing evidence for them from the literature. We will also suggest important areas for future research to assess their generality and predictive power.