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Diameter-growth model across shortleaf pine range using regression tree analysisAuthor(s): Daniel Yaussy; Louis Iverson; Anantha Prasad
Source: In: Amaro, Ana; Tome, Margarida, comps. Empirical and process-based models for forest tree and stand growth simulation. Edicoes Salamandra, Lisbon, Portugal: 479-498.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionDiameter growth of a tree in most gap-phase models is limited by light, nutrients, moisture, and temperature. Growing-season temperature is represented by growing degree days (gdd), which is the sum of the average daily temperatures above a baseline temperature. Gap-phase models determine the north-south range of a species by the gdd limits at the north and south boundaries of the realized niche. An assumption of these models is that a species will reach maximum diameter growth at the midpoint of its gdd range and that growth will taper parabolicallY to gdd limits. One might assume that diameter growth would increase toward the southern edge of a species realized niche, and that factors other than temperature would determine the southern boundary. The USDA Forest Service has remeasured the diameters of approximately 200 species of trees in the eastern United States, storing this information in a geo-referenced data base. Environmental data have been assembled from nationwide GIS coverages, including soils, digital elevation maps, climate data bases, and others. Using these data we developed and tested two methods in addition to the gap-phase model to model changes in annual diameter growth over the geographic range of species occurrence. Stepwise Regression (SR) and Regression Tree Analysis (RTA) were used to determine the environmental and geographic variables associated with different rates of diameter growth across the species range. SR provided a linear approach to model and predict diameter growth. RTA is an exploratory technique for uncovering structure in data and fitting models by recursive partitioning of the data. RTA is better at capturing interactions between variables than traditional linear models. These modeling techniques are demonstrated with shortleaf pine, Pinus echinata.
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CitationYaussy, Daniel; Iverson, Louis; Prasad, Anantha. 1999. Diameter-growth model across shortleaf pine range using regression tree analysis. In: Amaro, Ana; Tome, Margarida, comps. Empirical and process-based models for forest tree and stand growth simulation. Edicoes Salamandra, Lisbon, Portugal: 479-498.
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