Abstract:
Growth and yield models are a mainstay of forestry research and a necessary tool in the forest management decision process. Growth and yield models predict forest population dynamics over time and are an invaluable resource to forest managers making harvest and utilization decisions. At present, there are only a few growth models available for Alaska's coastal forests, all of which are either calibrated with even-aged data or outdated. Yield tables and growth models developed with even-aged data can be useful in even-aged management applications such as clear-cuts; however, these models are not able to predict the outcomes of uneven-aged silvicutural systems. The objective of this thesis is the development of a growth and yield model for coastal Alaska and computer applications to facilitate its use. A density-dependent, distance-independent, size- and species-specific matrix forest growth and yield model is calibrated with data collected on permanent sample plots located throughout coastal Alaska. The resulting growth and yield model enables short- and long-term predictions of stand basal area, volume, and biomass. Model assessment, with a focus on plausibility and accuracy, is evaluated on an independent dataset. Two computer programs (AlaskaPro and fgmod) are developed in conjunction with the new model. These programs can be used by forest researchers and land managers to compare the outcomes of various silvicultural prescriptions.
