From spatial community ecology to landscape genetics and genomics

从空间群落生态学到景观遗传学和基因组学

• 批准号: RGPIN-2017-04621
• 负责人: Legendre, Pierre
• 金额: $ 4.23万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=690771
• 关键词: spatial; community; ecology; landscape; genetics

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.

1.生态群落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，引起了生态学家的极大关注。尽管群落是多物种的，但它们的空间结构可以用一种名为空间特征函数分析的新的统计学方法来研究。使用这些方法，人们可以区分相互竞争、互补的过程，如环境过滤(基于生态位)和社区动态(中性)，这些过程构成了社区。*2.在我的研究中，我将社区生态学和统计学结合起来，开发了分析生态社区数据的新方法。自1975年以来，我一直在开发这个被称为数字生态学的领域，我写了这方面被引用最广泛的教科书。此外，我们正在开发软件，并为几个R包提供执行分析的功能。我们还在《使用R！》上发表了一本书。描述数字生态中使用的方法的系列。我们的R函数允许世界各地的研究人员快速使用新方法。在NSERC的下一个授予周期中，我们(我和我的实验室成员)将继续开发统计方法，以回答群落生态学、系统发生学和景观生态学/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.群落生态学的方法学发展将特别侧重于如何回答有关生态系统贝塔多样性的新问题。我们将把这些方法应用到保护区的研究中，并建议如何在面对气候变化时修改保护区的位置和形状。我们还将把重点放在生态相互作用网络上，并按照频率和贝叶斯方法为网络开发统计假设测试。我们还将把过去35年在空间群落生态学中发展起来的多变量分析方法转移到景观遗传学和基因组学中，补充进化遗传学已经提供的用于分析空间格局的相当复杂的工具箱。*4.在与拥有大量数据集的同事合作研究中，我们将进行空间、时间和时空贝塔多样性研究；我们的目标将是测试关于生态系统中产生和维持{生态、遗传、基因组}贝塔多样性的生态过程的假设。这些合作将为研究生和PDF的外展以及交流和共同指导创造机会。