Effect of Termination of Long-term Free Air CO2 Enrichment on Physiology and Carbon Allocation in a Loblolly Pine Dominated Forest

This dissertation examined the response to termination of CO2 enrichment of a forest ecosystem exposed to long-term elevated atmospheric CO2 condition, and aimed at investigating responses and their underlying mechanisms of two important factors of carbon cycle in the ecosystem, stomatal conductance and soil respiration. Because the contribution of understory vegetation to the entire ecosystem grew with time, we first investigated the effect of elevated CO2 on understory vegetation. Potential growth enhancing effect of elevated CO2 were not observed, and light seemed to be a limiting factor. Secondly, we examined the importance of aerodynamic conductance to determine canopy conductance, and found that its effect can be negligible. Responses of stomatal conductance and soil respiration were assessed using Bayesian state space model. In two years after the termination of CO2 enrichment, stomatal conductance in formerly elevated CO2 returned to ambient level, while soil respiration became smaller than ambient level and did not recovered to ambient in two years.

Path Optimization in Free Energy Calculations

Free energy calculations are a computational method for determining thermodynamic quantities, such as free energies of binding, via simulation.

Currently, due to computational and algorithmic limitations, free energy calculations are limited in scope.

In this work, we propose two methods for improving the efficiency of free energy calculations.

First, we expand the state space of alchemical intermediates, and show that this expansion enables us to calculate free energies along lower variance paths.

We use Q-learning, a reinforcement learning technique, to discover and optimize paths at low computational cost.

Second, we reduce the cost of sampling along a given path by using sequential Monte Carlo samplers.

We develop a new free energy estimator, pCrooks (pairwise Crooks), a variant on the Crooks fluctuation theorem (CFT), which enables decomposition of the variance of the free energy estimate for discrete paths, while retaining beneficial characteristics of CFT.

Combining these two advancements, we show that for some test models, optimal expanded-space paths have a nearly 80% reduction in variance relative to the standard path.

Additionally, our free energy estimator converges at a more consistent rate and on average 1.8 times faster when we enable path searching, even when the cost of path discovery and refinement is considered.