You are not currently logged in.
Access your personal account or get JSTOR access through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Long‐Distance Dispersal and Accelerating Waves of Disease: Empirical Relationships
Christopher C. Mundt, Kathryn E. Sackett, LaRae D. Wallace, Christina Cowger and Joseph P. Dudley
The American Naturalist
Vol. 173, No. 4 (April 2009), pp. 456-466
Stable URL: http://www.jstor.org/stable/10.1086/597220
Page Count: 11
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
Abstract: Classic approaches to modeling biological invasions predict a “traveling wave” of constant velocity determined by the invading organism’s reproductive capacity, generation time, and dispersal ability. Traveling wave models may not apply, however, for organisms that exhibit long‐distance dispersal. Here we use simple empirical relationships for accelerating waves, based on inverse power law dispersal, and apply them to diseases caused by pathogens that are wind dispersed or vectored by birds: the within‐season spread of a plant disease at spatial scales of <100 m in experimental plots, historical plant disease epidemics at the continental scale, the unexpectedly rapid spread of West Nile virus across North America, and the transcontinental spread of avian influenza strain H5N1 in Eurasia and Africa. In all cases, the position of the epidemic front advanced exponentially with time, and epidemic velocity increased linearly with distance; regression slopes varied over a relatively narrow range among data sets. Estimates of the inverse power law exponent for dispersal that would be required to attain the rates of disease spread observed in the field also varied relatively little (1.74–2.36), despite more than a fivefold range of spatial scale among the data sets.
© 2009 by The University of Chicago.