From numerical ecology to landscape genetics and genomics

生态群落的空间结构是环境条件、群落动态和历史动态共同作用的结果，是生态学家非常感兴趣的问题。社区由多元数据表表示；它们的空间结构可以用一种新的称为空间特征函数分析的统计方法来研究。使用这些方法，人们可以区分竞争和互补过程的行为，如环境控制（基于生态位）和构成社区的社区动态（中性）。在我的研究中，我将群落生态学和统计学相结合，开发了分析自然群落数据的方法。下面是两个例子。(1)相互依赖分析使研究人员能够确定环境变量对物种产生显著影响的尺度；我们将推广这种方法来检验关于整个生物群落的一整套环境变量的作用规模的假设。(2)第四个角分析将物种的特征与发现它们的环境特征联系起来。我们将推广该方法来预测物种在一个改变的生态系统中的发生，作为物种特征和环境特征的函数。我们的新R函数将允许世界各地的研究人员使用新方法。这项工作将包括一个新的R包，用于实现群落生态学中空间分析的各种方法。

The spatial structure of ecological communities is of great interest to ecologists because it results from the combined action of environmental conditions, community dynamics, and historical dynamics. Communities are represented by multivariate data tables; their spatial structure can be studied using a new family of statistical methods called spatial eigenfunction analysis. Using these methods, one can differentiate the actions of competing, complementary processes like environmental control (niche-based) and community dynamics (neutral) that structure communities. In my research, I combine community ecology and statistics to develop methods to analyse natural community data. Two examples follow. (1) Codependence analysis allows researchers to identify the scale(s) at which the effect of an environmental variable on a species is significant; we will generalize that method to test hypotheses about the scale of action of a whole set of environmental variables on a whole community of organisms. (2) The 4th-corner analysis links the traits of species to the characteristics of the environment where they are found. We will generalize that method to predict species occurrence in a modified ecosystem as a function of the species traits and the characteristics of the environment. Our new R functions will allow researchers around the world to use the new methods. The effort will include a new R package implementing a variety of methods for spatial analysis in community ecology.