Multivariate analysis of spatial patterns: a unified approach to local and global structures

We propose a new approach to the multivariate analysis of data sets with known sampling site spatial positions. A between-sites neighbouring relationship must be derived from site positions and this relationship is introduced into the multivariate analyses through neighbouring weights (number of neighbours at each site) and through the matrix of the neighbouring graph. Eigenvector analysis methods (e.g. principal component analysis, correspondence analysis) can then be used to detect total, local and global structures. The introduction of the D-centring (centring with respect to the neighbouring weights) allows us to write a total variance decomposition into local and global components, and to propose a unified view of several methods. After a brief review of the matrix approach to this problem, we present the results obtained on both simulated and real data sets, showing how spatial structure can be detected and analysed. Freely available computer programs to perform computations and graphical displays are proposed.     

Measuring biological diversity using Euclidean metrics

We study the complementary use of Rao's theory of diversity (1986) and Euclidean metrics. The first outcome is a Euclidean diversity coefficient. This index allows to measure the diversity in a set of species beyond their relative abundances using biological information about the dissimilarity between the species. It also involves geometrical interpretations and graphical representations. Moreover, several populations (e.g., different sites) can be compared using a Euclidean dissimilarity coefficient derived from the Euclidean diversity coefficient. These proposals are used to compare breeding bird communities living in comparable habitat gradients in different parts of the world.     

Matching species traits to environmental variables: a new three-table ordination method

This paper addresses the question of studying the joint structure of three data tablesR,L andQ. In our motivating ecological example, the central tableL is a sites-by-species table that contains the number of organisms of a set of species that occurs at a set of sites. At the margins ofL are the sites-by-environment data tableR and the species-by-trait data table Q. For relating the biological traits of organisms to the characteristics of the environment in which they live, we propose a statistical technique calledRLQ analysis (R-mode linked toQ-mode), which consists in the general singular value decomposition of the triplet (R ^t D _I LD _J Q,D _q ,D _p ) whereD _I ,D _J ,D _q ,D _p are diagonal weight matrices, which are chosen in relation to the type of data that is being analyzed (quantitative, qualitative, etc.). In the special case where the central table is analysed by correspondence analysis,RLQ maximizes the covariance between linear combinations of columns ofR andQ. An example in bird ecology illustrates the potential of this method for community ecologists.