Additive partitioning of diversity across hierarchical spatial scales in a forested landscape.
Ecological phenomena exist at multiple scales, but measurements of diversity frequently consider only the smallest scale, that of the original sample plots. Additive partitioning of diversity allows multiple, hierarchical spatial scales of analysis to reveal the scale at which diversity is maximized. We examined the spatial partitioning of diversity across 378 permanent plots established in 10 7-369-ha research natural areas (RNA) in the 294 455-ha Shawnee National Forest, Illinois, USA. Diversity (richness and Shannon's and Simpson's indices) was partitioned across four spatial scales, i.e. within and between plots, and between RNA and natural divisions (corresponding to α and three levels of β diversity), for two strata of vegetation (trees and woody understorey). For both strata, the highest contribution to diversity measured as species richness was between plots and between RNA. Diversity was lower than expected within plots, although Simpson's and Shannon's indices achieved their maximum values at this scale. However, Shannon's index values were higher than Simpson's index values at the between-RNA scale and for all strata, indicating that the most common species were found at this scale. There was a simple asymptotic relationship between plot occupancy and local abundance, suggesting high colonization rates and rapid colonization of open habitat indicative of a wide niche breadth of the most abundant species. Synthesis and applications. The implications of these findings are that maximum diversity across a forested landscape is not necessarily at the scale of sampling (i.e. within plots) but may be at higher scales corresponding to larger landscape units. Moreover, the largest contribution of richness to total diversity occurred at a larger scale than diversity expressed using measures based upon information theory, which incorporate species abundance and evenness. Conservation efforts seeking to preserve diversity must identify and target the correct scales to allow effective management. In this study, management at and within the RNA represents the most appropriate scale for conserving maximum diversity.