BRIDGING SCALES IN THE EVOLUTION OF INFECTIOUS DISEASE LIFE HISTORIES: APPLICATION
Article first published online: 21 JUL 2011
© 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.
Volume 65, Issue 11, pages 3298–3310, November 2011
How to Cite
Mideo, N., Nelson, W. A., Reece, S. E., Bell, A. S., Read, A. F. and Day, T. (2011), BRIDGING SCALES IN THE EVOLUTION OF INFECTIOUS DISEASE LIFE HISTORIES: APPLICATION. Evolution, 65: 3298–3310. doi: 10.1111/j.1558-5646.2011.01382.x
- Issue published online: 24 OCT 2011
- Article first published online: 21 JUL 2011
- Accepted manuscript online: 13 JUN 2011 08:06AM EST
- Received December 23, 2010, Accepted May 20, 2011
Within- and between-host disease processes occur on the same timescales, therefore changes in the within-host dynamics of parasites, resources, and immunity can interact with changes in the epidemiological dynamics to affect evolutionary outcomes. Consequently, studies of the evolution of disease life histories, that is, infection-age-specific patterns of transmission and virulence, have been constrained by the need for a mechanistic understanding of within-host disease dynamics. In a companion paper (Day et al. 2011), we develop a novel approach that quantifies the relevant within-host aspects of disease through genetic covariance functions. Here, we demonstrate how to apply this theory to data. Using two previously published datasets from rodent malaria infections, we show how to translate experimental measures into disease life-history traits, and how to quantify the covariance in these traits. Our results show how patterns of covariance can interact with epidemiological dynamics to affect evolutionary predictions for disease life history. We also find that the selective constraints on disease life-history evolution can vary qualitatively, and that “simple” virulence-transmission trade-offs that are often the subject of experimental investigation can be obscured by trade-offs within one trait alone. Finally, we highlight the type and quality of data required for future applications.