Testing the role of spatial structure in ecology and evolution

个人倾向于与接近他们或在他们的社会群体中的人进行更多的互动。类似地，在通过接触传播的疾病中，受感染的个体更有可能接触附近或其社会群体中的其他个体。因此，该疾病在人群中进行空间传播。许多计算机模型表明，这种空间传播可能对疾病动态产生重大影响。最近的研究表明，这种空间结构也可能对寄生虫的进化产生重要影响。如果它们在当地传播，它们被选择为较低的传播和毒性。要验证这种理论是非常困难的，但我们已经开发了一种昆虫病毒系统，在这种系统中，我们可以操纵宿主如何在本地移动。我们最近使用这个系统的工作证实了空间对传播的影响模型的预测;在粘性更强的人群中，它更低。我们现在要检查的作用，空间结构的共同进化的主机除了寄生虫。因此，我们建议建立计算机模型，并使用一些数学近似技术来预测本地感染对宿主抗性的影响。然后我们将用我们的昆虫病毒系统来测试这个。这种理论和生态实验的结合将使我们更清楚地了解不同程度的局部相互作用对致病生物进化的影响。鉴于人类学的变化正在改变许多种群的混合模式，我们必须了解这对野生动物和人类疾病的影响。

Individuals tend to interact more with individuals that are close to them or in their social group. Similarly in diseases that are spread by contact, infected individuals are more likely to contact other individuals that are close by or in their social group. The disease therefore spreads spatially through the population. There has been a number of computer models that show that this spatial spread can have a major effect on the disease dynamics. Recent work has shown that this spatial structure can also have important implications to the evolution of parasites. If they spread locally, they are selected for lower transmission and virulence. It is very difficult to test this sort of theory, but we have developed an insect virus system in which we can manipulate how locally the hosts move. Our recent work using this system has confirmed the predictions of the model of the effect of space on transmission; it was lower in a more viscose population. We now want to examine the role of spatial structure on the coevolution of the host in addition to the parasite. As such we propose to build computer models and use some mathematical approximation techniques to predict the effect of local infection on host resistance. We will then test this using our insect virus system. This mixture of theory and ecological experimentation will give us a much clearer idea of the implications of different degrees of local interactions on the evolution of disease causing organisms. Given that anthropological changes are altering the mixing patterns within many populations, it is important that we understand what the implications are to wildlife and human disease.