Estimating eastern spruce budworm dispersal using landscape genetics

森林昆虫的暴发给加拿大的森林带来了重大的经济和生态后果。研究森林昆虫与森林空间结构的相互作用，是更好地了解和控制森林昆虫种群动态的途径之一。通常，森林类型及其空间安排会影响昆虫种群的动态，并可能影响暴发潜力。云杉芽孢子虫是一种众所周知的森林害虫，在流行病期间可以摧毁大片森林。云杉芽孢子虫的暴发动态是由气候、森林、天敌和扩散之间的复杂相互作用形成的。尽管云杉芽孢虫对加拿大的森林很重要，但人们对它在不同森林条件下的传播及其对疫情的影响知之甚少。这项研究将使用遗传学方法来评估云杉芽孢子虫在不同森林类型中的扩散情况，方法是采用一套被称为“景观遗传学”的概念和分析方法。将利用昆虫之间的遗传相似性来评估在不同地理位置之间移动的可能性，以便创建云杉芽孢子虫作为森林结构函数的传播模型。使用这些技术，这项研究将增加我们对昆虫在不同森林和景观背景下如何变化的了解，将被用于改进预测昆虫种群动态和预测未来爆发风险的模拟模型。本次研究将在森林生态学、空间统计、模拟建模、景观遗传学等方法方面共培养11名高素质人才。

Outbreaks of forest insects have significant economic and ecological consequences for Canada's forests. One of the ways in which the population dynamics of forest insects can be better understood, and potentially controlled, is through examination of their interactions with forest spatial structure. Often, the type of forest and its spatial arrangement affects the dynamics of insect populations and can influence outbreak potential. The spruce budworm is an example of a well-known forest pest that can devastate huge areas of forests during epidemics. Spruce budworm outbreak dynamics are shaped by complex interactions among climate, forests, natural enemies, and dispersal. Despite the significance of the spruce budworm to Canada's forest, little is known about its dispersal in different forests conditions and how this affects outbreaks. This research will use genetic methods to assess dispersal in the spruce budworm in different forest types using a set of conceptual and analytical approaches referred to as "landscape genetics". Genetic similarity among insects will be used to assess the probability of movement among different geographic locations in order to create a model of spruce budworm dispersal as a function of forest structure. Using these techniques, this research will increase our knowledge of how insect movement varies in different forest and landscape contexts will be used to improve simulation models that predict insect population dynamics and forecast future outbreak risk. This research will train a total of 11 highly qualified personnel in methods of forest ecology, spatial statistics, simulation modelling, and landscape genetics.