From numerical ecology to landscape genetics and genomics

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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.