From spatial community ecology to landscape genetics and genomics

1.生态群落的空间结构是环境变量、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。尽管事实上，社区是多物种的，他们的空间结构可以使用一个新的家庭的统计方法称为空间本征函数分析进行研究。使用这些方法，人们可以区分竞争，互补过程，如环境过滤（生态位为基础的）和社区动态（中性），结构社区的行动。 2.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，开发了分析生态群落数据的新方法。从1975年起，我就一直在发展这个领域，称为数值生态学，我写了关于这个主题的最广泛引用的教科书。此外，我们正在开发软件，并为几个R软件包提供功能以进行分析。我们还出版了一本书在“使用R！”描述数值生态学中使用的方法的系列。我们的R功能使世界各地的研究人员能够快速使用新方法。在下一个NSERC资助周期，我们（我和我的实验室成员）将继续开发统计方法，以回答社区生态学，植物遗传学和景观生态学/遗传学/基因组学中的紧迫问题，我们将使用新的方法和软件分析全球规模的数据集。 3.群落生态学的方法发展将特别侧重于如何回答有关生态系统β多样性的新问题。我们将把这些方法应用到保护区的研究中，并建议在气候变化的情况下如何修改它们的位置和形状。我们还将专注于生态交互网络，并根据频率论和贝叶斯方法开发网络的统计假设测试。我们还将转移到景观遗传学和基因组学的多变量分析方法在空间群落生态学在过去的35年中，补充了相当复杂的工具箱已经从进化遗传学分析空间格局。 4.在与拥有广泛数据集（有时是全球范围的数据集）并正在寻求帮助进行分析的同事合作研究中，我们将进行空间，时间和时空β多样性研究;我们的目标是测试有关生态过程的假设，这些生态过程产生并维持生态系统中的{生态，遗传，基因组} β多样性。这些合作将为研究生和PDF的外联、交流和共同指导创造机会。

1. The spatial structure of ecological communities is of great interest to ecologists because it results from the combined action of environmental variables, community dynamics, and historical dynamics. Despite the fact that communities are multi-species, 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 filtering (niche-based) and community dynamics (neutral) that structure communities. 2. In my research, I combine community ecology and statistics to develop new methods to analyse ecological community data. I have been developing the field, called Numerical ecology, since 1975 and I wrote the most widely cited textbook on the subject. In addition, we are developing software and are contributing functions to several R packages to carry out the analyses. We also published a book in the “Use R!” series describing the methods used in Numerical ecology. Our R functions allow researchers around the world to quickly use the new methods. During the next NSERC granting cycle, we (members of my lab and I) will continue to develop statistical methods to answer pressing questions in community ecology, phylogenetics, and landscape ecology/genetics/genomics, and we will analyse global-scale data sets using new methods and software. 3. Methodological developments for community ecology will focus, in particular, on ways to answer new questions about beta diversity in ecosystems. We will apply these methods to the study of protected areas and suggest how their positions and shapes should be modified in the face of climate change. We will also focus on ecological interaction networks and develop tests of statistical hypotheses for networks following frequentist and Bayesian approaches. We will also transfer to landscape genetics and genomics the methods of multivariate analysis developed in spatial community ecology during the last 35 years, complementing the fairly sophisticated toolbox already available from evolutionary genetics to analyse spatial patterns. 4. In collaborative research with colleagues who have extensive data sets, sometimes at global scale, and are seeking help to analyse them, we will conduct spatial, temporal and spatio-temporal beta diversity studies; our objective will be to test hypotheses about the ecological processes that generate and maintain {ecological, genetic, genomic} beta diversity in ecosystems. These collaborations will create opportunities for outreach and for exchange and co-direction of graduate students and PDFs.