Abstract
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.
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Claims 8
Claim · model-written summary
The authors recommend multiscale sampling where possible.
Verbatim
e0“My results emphasize the importance of matching study objectives with methodology and suggest inclusion of multi-scale samples whenever possible.”
t0“vegetation in the Carolinas”
Claim · model-written summary
In longleaf pine communities, silt and soil pH were the strongest diversity predictors.
- Taxon
- longleaf pine
Pinus palustrise0 - Compound
- soil pHe0
- Outcome
- diversitye0
- Setting
- longleaf pine communitiese0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In longleaf pine communities, silt and soil pH were the strongest predictors of diversity”
Claim · model-written summary
Models with soil nutrients and texture predicted longleaf pine diversity well.
- Taxon
- longleaf pinePinus palustrise1
- Outcome
- predicted diversity welle0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“models with soil nutrients and texture predicted diversity well”
e1“In longleaf pine communities”
Claim · model-written summary
In mountain forests, soil pH and nutrients, especially calcium and manganese, were the best diversity indicators.
Not reportedDirection
Verbatim
e0“In mountain forests, soil pH and nutrients, especially calcium and manganese, were the best indicators of diversity.”
Claim · model-written summary
Structural equation models showed an influence of productivity on diversity.
Not reportedDirection
Verbatim
e0“Structural equation models demonstrated an influence of productivity on diversity.”
t0“vegetation in the Carolinas”
Claim · model-written summary
Mountain diversity models worked best with local predictors.
Verbatim
e0“Mountain models worked best with local predictors”
t0“vegetation in the Carolinas”
Claim · model-written summary
The best longleaf pine model combined local and regional predictors.
- Taxon
- longleaf pinePinus palustrise1
- Outcome
- best longleaf modele0
- Comparator
- local and regional predictorse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the best longleaf model had local and regional predictors”
e1“In longleaf pine communities”
Claim · model-written summary
Forest gap sampling gave some support to the resource context framework.
Verbatim
e0“Sampling of forest gaps provided some support for the resource context framework for gap dynamics.”
t0“vegetation in the Carolinas”