Showing papers by "Peter S. White published in 2016"

Forest structure as a predictor of tree species diversity in the North Carolina Piedmont

Abstract: Questions Can forest structure significantly predict tree species diversity in the forests of the North Carolina Piedmont? If so, which structural attributes are most correlated with it, and how effective are they when used in concert in a generalized predictive model of tree species diversity? Location North Carolina Piedmont, USA. Methods Using a set of geographically distributed Forest inventory and analysis (FIA) plots (n = 972), we analysed Spearman correlations between 15 measures of forest structure and five indices of tree species diversity. We predict tree species diversity based on structural predictors using support vector regression (SVR) models, assessing model fit via ten-fold cross-validation. Results Results show a consistent and significant relationship between most structural attributes and indices of tree species diversity. Among all structural predictors, maximum height, basal area size inequality (basal area Gini coefficient) and skewness of the basal area distribution (Weibull shape) exhibited the strongest correlations with indices of tree species diversity. Predictive SVR models trained solely with structural attributes explained 44–61% of the variance in tree species diversity in the full Piedmont data set, and 22–71% of the variance in subsets defined by stand origin and forest type. Conclusions Results confirm that forest structure alone was able to predict a substantial portion of the variance in tree species diversity without accounting for other known predictors of diversity in the North Carolina Piedmont, such as environment, soil conditions and site history. Beyond the theoretical implications of unravelling primary patterns underlying tree species diversity, these findings highlight the empirical basis and potential for utilizing forest structure in predictive models of tree species diversity over large geographic regions.

Investigation of a Catenane with a Responsive Noncovalent Network: Mimicking Long-Range Responses in Proteins

Abstract: We report a functional synthetic model for studying the noncovalent networks (NCNs) required for complex protein functions. The model [2]-catenane is self-assembled from dipeptide building blocks and contains an extensive network of hydrogen bonds and aromatic interactions. Perturbations to the catenane cause compensating changes in the NCNs structure and dynamics, resulting in long-distance changes reminiscent of a protein. Key findings include the notion that NCNs require regions of negative cooperativity, or “frustrated” noncovalent interactions, as a source of potential energy for driving the response. We refer to this potential energy as latent free energy and describe a mechanistic and energetic model for responsive systems.

Disturbance, Productivity, and Tree Characteristics in the Central Hardwoods Region

Abstract: Community theory proposes that the taxonomic diversity and characteristics, or traits, of the trees found within a particular forest community is a function of both the productivity and disturbance history of that community. The theory also predicts that niche differentiation to the conditions caused by disturbance is strongest on productive sites and decreases along productivity gradients. Therefore, both taxonomic and trait diversity should be highest on productive sites at scales that encompass a range of disturbance histories, and lowest on poor quality sites where environmental conditions are strong ‘filters’ for plant traits. Using a large data set from the USDA Forest Service, we examine patterns of taxonomic and tree trait diversity with respect to recent disturbance events and productivity across the Central Hardwood Region. Our analyses reveal strong regional trends in diversity, less pronounced trends along the productivity gradient, and little effect of disturbance on tree diversity and characteristics.

Structural and Compositional Change in Great Smoky Mountains National Park since Protection, 1930s–2000s

Abstract: When Great Smoky Mountains National Park (GSMNP) was placed under strict protection in 1934, about 20 % of the landscape was old-growth forest that had never been logged or farmed, and about 80 % was second growth recovering from logging and settlement. We might expect that the structure of GSMNP’s old-growth forests today would capture the natural range of variation of these southern Appalachian forests, subject only to localized natural disturbances such as landslides, flooding, windthrow, ice storms, and fire. Despite protection, however, multiple indirect, diffuse anthropogenic disturbances including exotic pests, atmospheric deposition, changes in herbivory, and changes in fire regime have continued to affect both old-growth and successional GSMNP forests. Here, we employ a mid-1930s vegetation survey and a compilation of more recent vegetation datasets (1990s–2000s) to compare the historic and present-day range of variation in GSMNP forest structure and composition. Widespread changes in structure reflect succession from historical disturbance and the overlay of continued disturbance, including in formerly undisturbed areas. Species indicative of disturbance and reduced fire frequency are abundant across the landscape in patterns reflecting the legacy of historical disturbance types, continued disturbance, and the interaction of these disturbances with environment.