From numerical ecology to landscape genetics and genomics

从数值生态学到景观遗传学和基因组学

• 批准号: 7738-2012
• 负责人: Legendre, Pierre
• 金额: $ 4.37万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=544441
• 关键词: numerical; ecology; landscape; genetics; genomics

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.

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，因而引起生态学家的极大兴趣。社区由多元数据表表示;其空间结构可以使用称为空间本征函数分析的新的统计方法来研究。使用这些方法，人们可以区分竞争的行动，互补的过程，如环境控制（生态位为基础的）和社区动态（中性），结构社区。在我的研究中，我将联合收割机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)共依赖分析使研究人员能够确定环境变量对物种的影响是显着的规模;我们将推广这种方法来测试关于一整套环境变量对整个生物群落的作用规模的假设。(2)第四角分析将物种的特征与它们所处的环境特征联系起来。我们将把这种方法推广到预测物种在改良生态系统中的出现，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，实现各种方法的空间分析在社区生态学。