Spatio-Temporal Dynamics of Stand Density Across Diverse Forests of the Continental United States

One of the robust and measures of stand density is Reineke’s (1933) stand density index (SDI), which is used for predicting stand development and self-thinning in single-species, even-aged stands and stand density management diagrams (SDMDs). The chapters in this study sought to: i) evaluate the development and application of SDI for the management of complex and adaptive forests; ii) estimate and map forest relative density for decision support across the Continental US (CONUS) and iii) quantifying the relative density-growth relationships across contrasting forest types of the CONUS. SDI has since been modified for application in multi-cohort and mixed composition stands to understand stand density-growth dynamics and guiding forest management operations. The dissertation synthesized literature about the modifications, statistical methods and necessary data needed for estimating SDI and it has been applied in multi-cohort and mixed stands. The modified versions of SDI have been applied in multi-cohort, mixed composition stands using robust statistical methods such as hierarchical Bayesian methods and linear quantile mixed modeling. The robust statistical methods incorporate ancillary data such as climate information and functional traits for example wood specific gravity, drought, and shade tolerance. There has been a shift towards permanent plot data and repeated measurements from national forest inventories. United States Department of Agriculture (USDA)’s Forest Inventory and Analysis (FIA) is responsible for providing characteristics and statistics regarding forest attributes and ecosystem processes at various strategic scales each with their own levels of precision and refinement. However, these characteristics and attributes are available at very coarse resolution across the CONUS. The study leveraged on a publicly available raster (TREEMAP; Scientific Data 8, 11) to produce map and estimate SDI, maximum SDI (SDIMAX) and RD at 30 x 30m resolution. The differences in FIA and TREEMAP derived estimates can be attributed to differences in the spatial resolution, underlying assumptions of each method of estimation, and spatial-temporal misalignment between the two sources of data. Based on the modification and applications done to the original SDI, was empirically tested the relationship between RD and different growth metrics. Gross growth and net growth linearly increased with increases in RD before reaching the breakpoint. Specifically, gross and net growth increased up to breakpoints of RD=0.52 (CI=0.51-0.54) and RD=0.51 (CI=0.5-0.52) respectively forest types and ecosubsections. Based on the population-level profiles, gross and net growth relationships became asymptotic after the breakpoint. Stand structure and density factors such as top height and total basal area were identified as the potential additional drivers of gross and net growth. Thus, the optimal RD by forest type and ecoregion for the RD-growth relationship can be used for making specific management objectives and decision-making processes across CONUS. Overall, this study demonstrated the refinement and application of Reineke’s SDI in the management of complex and adaptive forests. Additionally, the study showcased how national inventory data can be integrated with Geographical Information Systems and remote sensing to rapidly map and estimate stand density metrics in the CONUS. The optimal RD by forest type and ecoregion for the RD-growth relationship can be used for making specific management objectives and decision-making processes across CONUS.