Dehai Zhao

Tree mortality is shaped by tree characteristics, silvicultural treatments, and environmental conditions, the influences of which can change during stand development. In the southeastern United States, loblolly pine (\textit{Pinus taeda}) plays a critical role in providing timber production and ecosystem services. Understanding the impacts of abiotic and biotic factors on tree mortality is essential for ecological forecasting and adaptive forest management in the region. Survival analysis, focusing on time-to-event, provides a useful tool for assessing the long-term dynamics of factor influence on tree mortality. In this study, we evaluated the impacts of tree, stand, and climatic factors on tree mortality of loblolly pine over a 18-year study period using survival analysis. Furthermore, we divided the study period into three time intervals to evaluate how these impacts vary at different stages of stand development. Data were collected from culture-by-density field trials across three physiographic regions, resulting in a total of 47,485 unique trees and 221,270 repeated observations.Results showed that larger and dominant trees generally have a lower tree mortality risk, whereas the risk increases with higher site quality, greater stand density, and the presence of disturbances. Intensive management shows a greater mortality risk than operational management. Notably, tree size, dominance and region have significant impacts only in older stands. Regional variation in mortality can be partially explained by climate extremes, including acute extreme cold stress (lower 10-year minimum January temperatures) and heat stress (higher annual mean temperatures). This research provides insights on tree mortality dynamics and its impact factors as well as a quantitative tool for forest managers to assess tree mortality in sustainable forest management.

Stand basal area (BA) is a core component of stand-level growth and yield (G&Y) systems used to guide forest management and simulate stand dynamics under alternative silvicultural regimes. Traditional BA equations, often based on Schumacher-type or generalized Richards functions, assume asymptotic BA development with stand age, an assumption that may not hold for long-rotation and intensively managed plantations established at high planting densities. To address this limitation, we developed new BA prediction and projection equations for loblolly pine plantations across a wide range of planting densities and management intensities in the southeastern United States. The new equations were derived directly from relationships between quadratic mean diameter (Dq) and stand age or dominant height, avoiding the imposition of predefined asymptotic behavior. The resulting models explicitly incorporate the effects of planting density, management intensity, and site quality. Compared with traditional formulations, the new equations showed improved performance and captured both asymptotic growth and post-culmination decline, providing a more flexible and biologically realistic basis for evaluating alternative silvicultural strategies.

Stand-level growth and yield (G&Y) models are essential for guiding forest management and assessing the effects of silvicultural regimes on volume, biomass, and carbon dynamics. A key component of G&Y systems is the dominant height (HD) model, from which site index (SI) is derived. Although HD has traditionally been assumed to be relatively insensitive to stand density within a moderate range, plantation forests are established across wide ranges of planting densities and are often subjective to intensive management, both of which can influence HD development. Using long-term data from loblolly pine culture by density studies spanning broad planting densities and management intensities in the southeastern United States, we developed new HD models that satisfy three desirable properties: base-age invariance, compatibility between prediction and projection formulations, and logical behavior in approximate limits. Asymptotic height varies with inherent site quality, planting density, and management intensity, and the relative HD growth rate at a given age is influenced by management intensity. From these models, we derived both a base SI equation representing inherent site quality and an expressed SI equation reflecting the effects of planting density and management. The expressed SI increased with increasing initial planting density and management intensity. Distinguishing between base and expressed SI, particularly within stand-level G&Y systems, enables clear separation of site quality effects from those of planting density and management practices.