Creating Clines to Study Dispersal and Adaptation

克隆线是地理上相邻、具有不同遗传构成的种群之间的融合区域。强大的理论和统计方法可以用来研究维持自然斜坡的进化力量。在这里，我们证明了倾斜度也为扩散和选择的实验研究提供了一个强大的操控工具。我们发展了一个新的非平衡跃层动力学模型，适用于研究快速演化变化。我们使用该模型研究了科罗拉多州马铃薯甲虫对杀虫剂抗性的扩散和自然选择，科罗拉多州马铃薯甲虫是马铃薯作物的主要捕食者。我们将在马萨诸塞州和缅因州复制马铃薯田里创建抗性克隆，并用它们来测试关于适应空间变化的生境的假说，包括扩散模式和选择强度。以“避难所”为基础的减缓杀虫剂抗药性演变速度的新策略也依赖于对传播的了解。我们对扩散的测量直接应用于新的管理策略，以减缓土豆甲虫对杀虫剂的抗药性演变速度，我们的新方法可以应用于其他作物害虫系统。我们的成果将与马铃薯种植者分享，他们将直接受益。

Clines are regions of blending between geographically abutting populations with different genetic makeup. Powerful theoretical and statistical methods are available to study the evolutionary forces that maintain natural clines. Here we demonstrate that clines also provide a powerful manipulative tool for the experimental study of dispersal and selection. We develop a new model of non-equilibrium cline dynamics that is suitable for studying rapid evolutionary changes. We use the model to study dispersal and natural selection in insecticide resistance in the Colorado potato beetle, a major defoliator of potato crops. We will create resistance clines in replicate potato fields in Massachusetts and Maine, and use them to test hypotheses about adaptation to spatially varying habitats, including dispersal patterns and the strength of selection. New 'refuge'-based strategies to slow the rate that pesticide resistance evolves also depend on knowledge of dispersal. Our measurements of dispersal apply directly to new management strategies to slow the rate that resistance to pesticides evolves in potato beetles, and our new methods can be applied to other crop-pest systems. Our results will be shared with potato growers, who will directly benefit.