Estimating eastern spruce budworm dispersal using landscape genetics

森林昆虫的爆发对加拿大的森林造成了重大的经济和生态后果。可以更好地了解和潜在地控制森林昆虫种群动态的方法之一是通过检查它们与森林空间结构的相互作用。通常，森林类型及其空间安排会影响昆虫种群的动态，并可能影响爆发的可能性。云杉budworm是一种众所周知的森林害虫，在流行期间可以摧毁大片森林。云杉芽虫的爆发动态是由气候、森林、天敌和扩散之间复杂的相互作用形成的。尽管云杉budworm对加拿大的森林很重要，但人们对它在不同森林条件下的扩散以及它如何影响疾病爆发知之甚少。本研究将使用一套被称为“景观遗传学”的概念和分析方法，利用遗传学方法评估云杉芽虫在不同森林类型中的扩散。昆虫之间的遗传相似性将用于评估在不同地理位置之间移动的概率，以便创建云杉budworm作为森林结构函数的传播模型。利用这些技术，这项研究将增加我们对昆虫运动在不同森林和景观背景下如何变化的了解，并将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。本研究将培养森林生态学、空间统计学、模拟建模、景观遗传学等方面的高素质人才11人。

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.