Accounting for Stand Variability in LMS: A case study on San Juan Island Braun, Sarah
Forest growth models are important tools used to support management decisions and to answer research questions. They can estimate future forest conditions under different scenarios, and therefore help predict outcomes of management practices and test hypotheses at both a stand and a landscape level. This study compared three different methods of inputting data from San Juan Island National Historical Park into a forest growth model (the Forest Vegetation Simulator) using the Landscape Management System (LMS). These methods were: (M1) the ‘plot-by-plot’ method, in which each plot per stand were input into the model as independent stands and the outcomes at the time of projection were averaged together; (M2) the ‘summed-plot’ method, in which all the plots per stand were input into the model as a single large plot; and (M3) the ‘representative-plot’ method, in which a single plot was selected based on a lowest squared Euclidean distance to represent the entire stand in the model. Five variables were considered in the analysis of the different model outputs at three projections: 20, 40, and 60 years into the future. The five variables that were considered were total stand volume, and volume by species; total basal area (BA), and BA by species; and total mean DBH. The results of this study showed that if the stands had been stratified appropriately prior to sampling, and if there was little spatial variation between plots within a stand, then the results from M1 were statistically the same as M2 and as M3. Differences between M1 and M2 were only observed with regards to total DBH, for one highly variable stand. Differences between M1 and M3 were observed in the outputs of BA by species, and volume by species. The only stand which resulted in no differences between the outputs of M1 and M3 was very uniform in density, species composition and structure. If LMS were adapted to more easily incorporate the inclusion of multiple plots per stand, field sampling would consistently be more efficient.
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