Skarpaas Olav, Shea Katriona
Department of Biology and Intercollege Graduate Degree Program in Ecology, Pennsylvania State University, University Park, Pennsylvania 16802, USA.
Am Nat. 2007 Sep;170(3):421-30. doi: 10.1086/519854. Epub 2007 Jul 16.
Understanding and predicting population spread rates is an important problem in basic and applied ecology. In this article, we link estimates of invasion wave speeds to species traits and environmental conditions. We present detailed field studies of wind dispersal and compare nonparametric (i.e., data-based) and mechanistic (fluid dynamics model-based) dispersal kernel and spread rate estimates for two important invasive weeds, Carduus nutans and Carduus acanthoides. A high-effort trapping design revealed highly leptokurtic dispersal distributions, with seeds caught up to 96 m from the source, far further than mean dispersal distances (approx. 2 m). Nonparametric wave speed estimates are highly sensitive to sampling effort. Mechanistic estimates are insensitive to sampling because they are obtained from independent data and more useful because they are based on the dispersal mechanism. Over a wide range of realistic conditions, mechanistic spread rate estimates were most sensitive to high winds and low seed settling velocities. The combination of integrodifference equations and mechanistic dispersal models is a powerful tool for estimating invasion spread rates and for linking these estimates to characteristics of the species and the environment.
理解和预测种群扩散速率是基础生态学和应用生态学中的一个重要问题。在本文中，我们将入侵波速的估计与物种特征和环境条件联系起来。我们展示了对风传播的详细实地研究，并比较了两种重要入侵杂草——新疆飞廉和刺飞廉的非参数（即基于数据的）和机械（基于流体动力学模型的）扩散核及扩散速率估计。一项精心设计的诱捕实验揭示了高度尖峰态的扩散分布，种子在距离源地96米处被捕获，远超过平均扩散距离（约2米）。非参数波速估计对采样力度高度敏感。机械估计对采样不敏感，因为它们是从独立数据中获得的，而且更有用，因为它们基于扩散机制。在广泛的现实条件下，机械扩散速率估计对强风和低种子沉降速度最为敏感。积分差分方程和机械扩散模型的结合是估计入侵扩散速率以及将这些估计与物种和环境特征联系起来的有力工具。
