Graduate Thesis Or Dissertation
 

Disaggregative and individual-tree growth models in theory and application

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/44558h83v

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  • Disaggregative and individual-tree/distance-independent modeling methods are compared and contrasted. Differences between the two are related to differences in functional and apparent resolution and may be illustrated using aggregation theory. When considering models of different levels of resolution describing a given phenomenon, invariance with respect to the aggregation implied (symmetry) may be important to both the modeler and the user alike. In the absence of invariance between stand and tree-level predictions, conflicting predictions arise. Some limitations of whole-stand models are also present in disaggregative models, however one way to bridge the gap between traditional individual-tree/distance-independent and whole stand models may be to use the individual-tree growth function in concert with the whole stand projection model as a disaggregator of growth. While the use of individual-tree models in this way is intuitively appealing, findings in this paper indicate that the more traditional, unconstrained tree growth functions may better predict growth of individual-trees. Five-year growth data from 105 Douglas-fir stands in western Oregon were used to compare various individual-tree, disaggregative and whole stand models at both the stand and tree level. Traditional approaches to disaggregation were unable to match the individual tree growth rate functions for predicting five-year growth rate of individual-trees. Both suffered due to a lack of any index of tree position, lack of a crown ratio component, and reliance on linearity between growth and tree dimension. The very simple proportional allocation approach based on tree dimension was most unsatisfactory. It remains to be seen if these trends will also hold for longer term projections of growth. At the stand level, the symmetric disaggregative model reduces to a simple whole stand approach to modeling. With respect to whole stand predictions, the individual-tree gross basal area growth model resulted in lower mean squared error than the aggregate (whole stand) model. There is a loss of information associated with the aggregation of tree data to predict growth. This loss of information may be responsible for the differences in mean squared error. Some of this difference may also be attributed to the inclusion of crown ratio in individual-tree predictions of growth.
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