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    We used data from regional forest inventories and research programs, coupled with mapped climatic and topographic information, to explore relationships and develop multiple linear regression (MLR) and regression tree models for total and deciduous shrub cover in the Oregon coastal province. Results from both types of models indicate that forest structure variables were most important for explaining both total and deciduous shrub cover. Four relationships were noted: (1) shrub cover was negatively associated with Tsuga heterophylla basal area and density of shade-tolerant trees; (2) shrub cover was negatively associated with variables that characteristically peak during stem exclusion and mid-succession; (3) shrub cover was positively associated with variables that characterize later successional stages; and (4) higher total and especially deciduous shrub cover were positively associated with hardwood stands. Environmental variables were more important for explaining deciduous shrub cover compared to total shrub cover, but they have an indirect effect on total shrub cover by influencing tree composition. However, because of land ownership patterns, it was difficult to decouple environmental from disturbance factors associated with management strategies across multiple ownerships.

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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Kerns, Becky K.; Ohmann, Janet L. 2004. Evaluation and prediction of shrub cover in coastal Oregon forests (USA). Ecological Indicators. 4: 83-98.


    forest understory, linear regression, Pacific Northwest, predictive models, regression tress, shrubs

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