Species diversity of vegetation in the Carolinas: The influence and interaction of scale of observation, soil nutrients, and disturbance events

Ecological determinants of diversity operate across a variety of scales with impacts ranging from small- scale, local influences to ecosystem-wide effects. Because there is no way to know a priori at which scale specific mechanisms influence diversity, observation at multiple scales is essential. In addition, consideration of the influence of resource availability is necessary, in part because the distribution of soil nutrients can be intrinsically spatially patchy, or patchy as a consequence of small-scale disturbance. Although previous studies of forest disturbances and diversity have focused on light, soil nutrient and water availability also affect vegetation response. Thus, to understand patterns in forest diversity it is essential to consider simultaneously scale of observation, disturbance, and availability of resources including light, water, and soil nutrients.;I provide a review and evaluation of multi-scale methodologies for observing forest diversity that can be used as a guide for researchers, managers, and conservationists. I then examine the relationship between soil nutrients and diversity across scales in longleaf pine and southern Appalachian vegetation. I next use structural equation modeling to investigate the productivity-diversity relationship. The previous sections are brought together to cast a new paradigm for how to view gap dynamics within a framework that considers the resource context and its impact on the role of subordinate species as filters and in the context of the structural carbon-nutrient balance hypothesis. Finally, I provide an initial test of this framework with field data from North Carolina forest gaps.;My results emphasize the importance of matching study objectives with methodology and suggest inclusion of multi-scale samples whenever possible. In longleaf pine communities, silt and soil pH were the strongest predictors of diversity, and models with soil nutrients and texture predicted diversity well. In mountain forests, soil pH and nutrients, especially calcium and manganese, were the best indicators of diversity. Structural equation models demonstrated an influence of productivity on diversity. Mountain models worked best with local predictors, while the best longleaf model had local and regional predictors. Sampling of forest gaps provided some support for the resource context framework for gap dynamics.