Finding essential scales of spatial variation in ecological data: a multivariate approach

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Niveau: Supérieur, Doctorat, Bac+8
Finding essential scales of spatial variation in ecological data: a multivariate approach Thibaut Jombart, Stephane Dray and Anne-Beatrice Dufour T. Jombart (), S. Dray and A.-B. Dufour, CNRS, UMR5558, Laboratoire de Biometrie et Biologie Evolutive, Univ. Lyon, FR-69000, Lyon, France, Univ. Lyon 1, FR-69622, Villeurbanne, France. The identification of spatial structures is a key step in understanding the ecological processes structuring the distribution of organisms. Spatial patterns in species distributions result from a combination of several processes occuring at different scales: identifying these scales is thus a crucial issue. Recent studies have proposed a new family of spatial predictors (PCNM: principal coordinates of neighbours matrices; MEMs: Moran's eigenvectors maps) that allow for modelling of spatial variation on different scales. To assess the multi-scale spatial patterns in multivariate data, these variables are often used as predictors in constrained ordination methods. However, the selection of the appropriate spatial predictors is still troublesome, and the identification of the main scales of spatial variation remains an open question. This paper presents a new statistical tool to tackle this issue: the multi-scale pattern analysis (MSPA). This ordination method uses MEMs to decompose ecological variability into several spatial scales and then summarizes this decomposition using graphical representations.

  • identified all

  • mspa

  • mems

  • quantitative variables

  • canonical approaches

  • eigenvalues eigenvalues

  • spatial predictors

  • partial canonical



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