"The function, composition, and health of regional forest systems are driven by factors operating at a range of spatio-temporal scales. Climate shapes regional species composition at centennial-to-millennial timescales, but may also contribute to more rapid forest change through the occurrence of climate extremes. Disturbance events operate at scales ranging from individual trees to landscape-level metacommunities impacting forest dynamics and resetting forest succession and development over decadal-to-centennial time frames. At the local-scale, forest function, composition, and health at a given time are determined by forest demographic processes including growth, mortality, and regeneration. Understanding and predicting broad-scale patterns of forest change requires methods to integrate these different factors synthesizing information across spatio-temporal scales. The research presented here focuses on the development and application of spatio-temporal, Bayesian hierarchical methods to advance understanding of the processes and factors driving large-scale forest change. The methods seek to make inference about latent forest processes of interest based on noisy observations of forest demographics, climate, and disturbance events. The impacts of novel climatic conditions forecast to occur over the next century on forest ecosystem function are difficult to predict given potential interactions between climate, disturbance events, and forest characteristics such as species composition, density, and tree size/age distribution. The first three chapters of the following dissertation focus on the development and application of methods to advance understanding of such interactions. First, a dynamic Bayesian hierarchical model is presented allowing forest growth responses to climate variables to vary over time in relation to past climate extremes, disturbance events, and forest dynamics. The model was applied to tree-ring data from a range of sites within northeastern Minnesota. Results revealed significant growth responses to soil water availability triggered by large climatic water deficits across multiple seasons and years, forest tent caterpillar defoliation events, and high forest density following large regeneration events. Building on these results, the interactive effects of past water deficit and insect defoliation stress on forest growth were further explored using broad-scale tree-ring and defoliation data from two regions of the Canadian boreal forest with contrasting species compositions, primary insect defoliators, and regional climates. A series of novel methods were developed to quantify the ecological memory of boreal trees to antecedent water and insect defoliation stress. Results highlighted the temporal persistence of drought and defoliation stress on boreal tree growth dynamics and provided an empirical estimate of their interactive effects. Finally, a Bayesian state space framework for the assimilation of tree-ring and forest inventory data with a forest growth and yield model (Forest Vegetation Simulator) was developed to reconstruct forest dynamics with explicit uncertainty. The framework allows for the use of tree-ring data to inform growth-climate relationships and inventory data to inform estimates of past forest composition, density, and tree size/age distribution. The unique inference afforded by the framework is demonstrated through its application to red pine plantation data from northern Minnesota. The final chapter of the dissertation presents a Bayesian point process model for the reconstruction of past fire regimes using sediment charcoal data. The framework was applied to a network of boreal forest lakes in interior Alaska demonstrating a significant reduction in the uncertainty of past fire identification compared to existing methodologies. Further, results highlighted shifts in the regional fire regime coincident with changes in regional species composition over the past 223C10,000 years. The methods developed herein and their application to a range of forest data types provide increased understanding of the multi-scale factors contributing to changes in forest growth and mortality over time and space. Still missing, however, is a process-based framework that integrates the various spatio-temporal methods presented to gain mechanistic understanding of forest responses to extreme climate and disturbance events. Future work is needed to develop such a framework and apply it to extensive regional forest data sets to advance mechanistic understanding and predict forest responses to the novel environmental conditions of the 21st century."--Pages ii-iii.
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