The ability to predict wood properties would aid in the growing of southern pine timber for specific end uses. Three wood properties, specific gravity, shrinkage, and knottiness, were chosen as the focus of this study. Silvicultural studies focusing on southern pine management were researched for any information on their impacts on wood properties. The information from silvicultural studies was then used to evaluate growth and yield models for ease of adaptation to predict wood properties. The information necessary to predict all wood properties is not currently available. Although, specific gravity has significantly more information available than the other properties and it is probably the most predictable.

No description available.

This dissertation addresses critical challenges in forest management and restoration in the Lower Mississippi Alluvial Valley (LMAV) through a series of interconnected studies focused on improving habitat suitability modeling and growth prediction for oak species. The research employs advanced modeling techniques to enhance our understanding of species-habitat relationships and forest dynamics in the context of climate change. Initial studies focused on developing ensemble habitat suitability models for American chestnut (Castanea dentata) and butternut (Juglans cinerea), two historically important but currently threatened tree species. These models predict suitable habitats and potential range shifts under various climate change scenarios, highlighting the species' vulnerabilities and informing conservation strategies. Building on these approaches, the research expands to model habitat suitability for eight key oak species in the LMAV. This multi-species analysis reveals both shared and distinct ecological requirements among the oak species, providing valuable insights for targeted restoration efforts. Niche overlap analysis further elucidates potential species interactions and habitat partitioning within the region. The dissertation culminates in the development of improved climate-sensitive growth and yield models for bottomland oaks. By incorporating habitat suitability predictions as a modifier, these models demonstrate significantly enhanced accuracy compared to traditional approaches. This integrated modeling framework offers a more comprehensive understanding of oak growth dynamics under changing environmental conditions. Throughout the research, the importance of key environmental drivers, such as temperature, precipitation, and soil characteristics, is consistently highlighted. The studies also underscore the potential impacts of climate change on species distributions and forest composition in the LMAV. This research contributes to the advancement of forest modeling techniques and provides practical insights for sustainable forest management, conservation of threatened species, and climate change adaptation strategies. The findings have important implications for decision-making in forestry and conservation, particularly in the LMAV but with potential applications to other regions. Future research directions are suggested, including the use of higher-resolution datasets and validation across diverse ecosystems to further improve model applicability and accuracy.

Trees compete for various resources such as sunlight, water, and nutrients, which can be expressed as numerical terms, called competition indices (CI). Competition between individual trees is correlated with their growth and mortality. Therefore, CIs are used as independent variables to develop, improve and modify growth and yield models. This study was conducted to test the effect of neighborhood competition on tree diameter growth among Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), western hemlock (Tsuga heterophylla (Raf.) Sarg) and red alder (Alnus rubra Bong.), in the Pacific Northwest Coast Range, USA. After testing seven distance-independent CIs and three distance-dependent CIs, only the distance-independent CIs were found to significantly affect the diameter growth model. Among them, CIs with basal area and diameter information were the most impactful. As a result, a simple CI was very effective in a model that accounts for the basal area information of different tree species.