Reference number : GC-2017-Q1-02112, amendment number : , agreement type : G, recipient legal name : Legendre, Pierre (Université de Montréal), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : 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 &ldquo;Use R!&rdquo; 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., program purpose french : 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 &ldquo;Use R!&rdquo; 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., agreement title english : From spatial community ecology to landscape genetics and genomics, agreement title french : From spatial community ecology to landscape genetics and genomics, agreement number : RGPIN, agreement value : 290000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada