Partitioning Species Diversity across Landscapes and Regions: A Hierarchical Analysis of a, b, and g Diversity

Species diversity may be additively partitioned within and among samples (a and b diversity) from hierarchically scaled studies to assess the proportion of the total diversity (g) found in different habitats, landscapes, or regions. We developed a statistical approach for testing null hypotheses that observed partitions of species richness or diversity indices differed from those expected by chance, and we illustrate these tests using data from a hierarchical study of forest-canopy beetles. Two null hypotheses were implemented using individualand sample-based randomization tests to generate null distributions for a and b components of diversity at multiple sampling scales. The two tests differed in their null distributions and power to detect statistically significant diversity components. Individual-based randomization was more powerful at all hierarchical levels and was sensitive to departures between observed and null partitions due to intraspecific aggregation of individuals. Sample-based randomization had less power but still may be useful for determining whether different habitats show a higher degree of differentiation in species diversity compared with random samples from the landscape. Null hypothesis tests provide a basis for inferences on partitions of species richness or diversity indices at multiple sampling levels, thereby increasing our understanding of how a and b diversity change across spatial scales.