Effects of introduced moose (Alces alces) on vegetation composition, nutrient dynamics, and decomposition rates in boreal forest ecosystems in Newfoundland, Canada

Globally, consumers affect ecosystem processes including nutrient dynamics. Herbivores have been known to slow nutrient flow in boreal forest ecosystems. I examined the effects of introduced moose on disturbed forests of Newfoundland, Canada by conducting a field experiment during August - November 2014 in 20 paired moose exclosure-control plots. I tested whether moose browsing directly and indirectly affected forests by measuring plant species composition, litter quality and quantity, soil quality, and decomposition rates in areas moose exclosure-control plots. I analyzed moose effects using linear mixed effects models and found evidence indicating that moose reduce plant height and litter biomass affecting the availability of carbon, nitrogen, and phosphorus. However, plant diversity, soil quality, and litter decomposition did not differ between moose exclosures and controls. Moose in Newfoundland directly influence plant regeneration and litter biomass while indirect effects on soil ecosystems may be limited by time, disturbance, and climate.

Dynamic fault tree analysis with PEWMA modeling of event rates and Bayesian updating of material balance parameters using MCMC

This research explores Bayesian updating as a tool for estimating parameters probabilistically by dynamic analysis of data sequences. Two distinct Bayesian updating methodologies are assessed. The first approach focuses on Bayesian updating of failure rates for primary events in fault trees. A Poisson Exponentially Moving Average (PEWMA) model is implemnented to carry out Bayesian updating of failure rates for individual primary events in the fault tree. To provide a basis for testing of the PEWMA model, a fault tree is developed based on the Texas City Refinery incident which occurred in 2005. A qualitative fault tree analysis is then carried out to obtain a logical expression for the top event. A dynamic Fault Tree analysis is carried out by evaluating the top event probability at each Bayesian updating step by Monte Carlo sampling from posterior failure rate distributions. It is demonstrated that PEWMA modeling is advantageous over conventional conjugate Poisson-Gamma updating techniques when failure data is collected over long time spans. The second approach focuses on Bayesian updating of parameters in non-linear forward models. Specifically, the technique is applied to the hydrocarbon material balance equation. In order to test the accuracy of the implemented Bayesian updating models, a synthetic data set is developed using the Eclipse reservoir simulator. Both structured grid and MCMC sampling based solution techniques are implemented and are shown to model the synthetic data set with good accuracy. Furthermore, a graphical analysis shows that the implemented MCMC model displays good convergence properties. A case study demonstrates that Likelihood variance affects the rate at which the posterior assimilates information from the measured data sequence. Error in the measured data significantly affects the accuracy of the posterior parameter distributions. Increasing the likelihood variance mitigates random measurement errors, but casuses the overall variance of the posterior to increase. Bayesian updating is shown to be advantageous over deterministic regression techniques as it allows for incorporation of prior belief and full modeling uncertainty over the parameter ranges. As such, the Bayesian approach to estimation of parameters in the material balance equation shows utility for incorporation into reservoir engineering workflows.