Growing concerns over the possible effects of greenhouse-gas-related global warming on North American forests have led to increasing calls to address climate change effects on forest vegetation in management and planning applications.  The objectives of this project are to model contemporary conditions of soils and climate associated with the presence or absence and abundance of five southern pine species: shortleaf pine (Pinus echinata Mill.), slash pine (P. elliottii Engelm.), longleaf pine (P. palustris Mill.), pond pine (P. serótina Michx.), and loblolly pine (P. taeda L.).  Classification and regression based Random Forest models were developed for presence-absence and abundance data, respectively.  Model and diagnostics such as receiver operating curves (ROC) and variable importance plots were examined to assess model performance.  Presence-absence classification models had out-of-bag error rates ranging from 6.32% to 16.06%, and areas under ROC curves ranging from 0.92-0.98.  Regression models explained between 13.76% and 43.31% of variation in abundance values.  Using the models based on contemporary data, predictions were made for the future years 2030, 2060, and 2090 using four different greenhouse gas emissions scenarios and three different general circulation models.  Maps of future climate scenarios showed a range of potential changes in the geographic extent of the conditions consistent with current presence observations.  Results of this work will be incorporated into eastern U.S. variants of the Forest Vegetation Simulator (FVS) model, similar to work that has been done for FVS variants in the West. / Master of Science

Three general questions are studied regarding plant carbon allocation strategies. (1) The R* Rule states that the superior competitor in a plant community should exclude all others by minimizing available limiting nutrient concentration below the level needed for survival of its competitors. I asked whether a plant carbon allocation strategy that minimizes the concentration of available limiting nutrient is consistent with Lotka's (1922) conjecture that ecosystems should evolve to maximize total energy flow (primary production). (2) In landscapes such as the Everglades, areas of landscape with higher energy flow (primary production) than the surrounding area also have higher available concentrations of limiting nutrient, rather than lower concentrations, which might be expected from the R* rule. I asked whether this pattern can be explained. (3) I asked how optimal allocation of carbon to plant defense allocation strategies might depend on different conditions of nutrient availability, shading, and herbivory. To address all three questions, I used a model revised from the G'DAY model (Comins and McMurtrie 1993) to study tree allocation of carbon resources between foliage, roots, and defense. With regard to the first question, I found that the allocation strategy that leads to minimum concentration of available nutrients is the same as the strategy for which energy flux to roots, rather than total energy flux, is maximized. Further, I found that the strategy that was competitively dominant was neither the strategy for which total energy flux was maximized, nor that for which available nutrient concentration was minimized. With regard to the second question, I found that, if a patch of vegetation on a landscape is able to capture nutrients from the surrounding landscape, for example, through relatively higher evapotranspiration, it could lead to the opposite of what is expected from the R* rule; that is, available limiting nutrient concentration is maximized when carbon flow to the roots is maximized. With regard to the last question, I found that under high herbivory, the optimal plant strategy for allocation of carbon to defense depends on the available nutrient concentration and amount of radiation to the plant, in agreement with some theoretical predictions.

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