Hierarchical, multi-scale decomposition of species-environment relationships

We present an adaptation of existing variance partitioning methods to decompose species-environment relationships in hierarchically-structured, multi-scaled data sets. The approach translates a hierarchical, multi-scale conceptual model into a statistical decomposition of variance. It uses a series of partial canonical ordinations to divide the explained variance in species-environment relationships into its independent and confounded components, facilitating tests of the relative importance of factors at different organizational levels in driving system behavior. We discuss the method in the context of an empirical example based on forest bird community responses to multiple habitat scales in the Oregon Coast Range, USA. The example presents a two-tiered decomposition of the variation in the bird community that is explainable by a series of habitat factors nested within three spatial scales (plot, patch, and landscape). This method is particularly suited for the problems of hierarchically structured landscape data. The explicit multi-scale approach is a major step forward from conducting separate analyses at different scale levels, as it allows comprehensive analysis of the interaction of factors across scales and facilitates ecological interpretation in theoretical terms.