Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Abstract

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 heterophyllabasal 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. Tree models performed similarly (PRD = 0.17–0.27) or better compared to MLR models (PRD = 0.17–0.23) although they contained more (2) predictor variables. Our results indicate that response variable transformation can greatly improve regression tree model performance. While interpretation of MLR and tree models were somewhat similar, the tree models allowed a more explicit understanding of relationships and provided thresholds for anticipating shifts in shrub cover. Such thresholds are useful to forest managers who are monitoring and evaluating critical amounts of shrub cover necessary for different ecosystem components such as bird habitat. Lack of strong predictive power in both types of models may be because many common shrubs can persist and maintain consistent cover under a variety of stand and environmental conditions or there may be a lag time between disturbance events and shrub response. The stochastic nature of disturbance and their interactions with site conditions also makes prediction at this scale in this highly managed landscape inherently problematic. Yet, our models provide both a predictive and conceptual tool for understanding shrub cover patterns across the region. © 2004 Elsevier Ltd. All rights reserved.