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。森林昆虫种群动态的方法之一，可以更好地理解，并可能控制，是通过检查它们与森林空间结构的相互作用。森林的类型及其空间安排往往影响昆虫种群的动态，并可能影响爆发的可能性。云杉芽虫是一种众所周知的森林害虫，在流行期间可以破坏大片森林。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。尽管云杉芽虫对加拿大森林的重要性，但对其在不同森林条件下的传播以及如何影响疫情知之甚少。这项研究将使用遗传学的方法来评估在云杉芽虫在不同的森林类型，使用一套概念和分析方法被称为“景观遗传学”的传播。昆虫之间的遗传相似性将被用来评估不同地理位置之间的运动的概率，以创建一个模型的云杉芽虫传播的森林结构的功能。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环境中如何变化的了解，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。这项研究将在森林生态学、空间统计学、模拟建模和景观遗传学方法方面培训总共11名高素质人员。

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.****