Mechanisms of Disease Transmission, Variability in Host Susceptibility, and Forest Defoliator Outbreaks
Mechanisms of Disease Transmission, Variability in Host Susceptibility, and Forest Defoliator Outbreaks
批准号:
0516327
负责人:
Gregory Dwyer
金额:
$0.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-09-01 至 2010-08-31
中文摘要
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英文摘要
Many forest insects undergo outbreaks, in which soaring populations defoliate forests, leading to extensive economic damage. Outbreaks often come to an end naturally, when epidemics of viral diseases sweep through the population, causing massive mortality. Why do such diseases not simply keep the population in check permanently? What determines when epidemics begin? Answers to these questions would help us control pest insects without insecticides, and might help us understand other diseases as well. Our approach to finding the answers is to carry out experiments, using a viral disease of gypsy moths. The gypsy moth is an introduced pest of hardwood trees in the Northeastern U.S. and Canada, and it is spreading rapidly. The viral disease causes gypsy-moth larvae to literally melt onto foliage, spreading virus particles to be consumed by other hapless larvae. Our work focuses on three factors that may affect the spread of this disease. First, we are testing whether the insect evolves resistance over time, because individuals that survive the epidemic may be those that can better fight off infection. Second, we are testing whether transmission is affected by the pattern of virus on leaves, so that spread rates rise when the virus is distributed more uniformly. Third, we are testing whether chemical compounds in the leaves that the insects feed on may alter the chance that insects become infected. Each test consists of a set of experiments in which insects feed on virus-contaminated leaves in nature, except that we manipulate each factor in turn; the genetic background of the insect, the pattern of virus on the leaves, and the chemical composition of the leaves. We will then synthesize our results using computer models that translate the data into predictions of rates of disease spread.
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