Quantifying the Influence of Stand Spatial Structure and Species Composition on Forest Growth and Regeneration Patterns: Evaluating the Role of Distance-Dependent Competition Indices within the Acadian Variant of the Forest Vegetation Simulator
In most forests, growth rates vary markedly from tree to tree. This variation strongly influences forest stand development and productivity and leads to diversity in tree sizes and spatial patterns. Understanding the controls over tree growth variation is critical for developing reliable computer models for predicting forest growth and yield, needed for improving management guidelines.
Competition for resources (light, water, nutrients) is a very well-studied source of variation in individual tree growth. Tree to tree competition is influenced by a variety of factors such as tree age, size, genetics, micro-environment, and characteristics of neighboring trees. Tree spatial patterns and species composition clearly influence forest growth and regeneration. However, most growth and yield models ignore stand spatial structure and species composition. NSRC researchers will characterize the influence of tree spatial information and species composition on tree growth and regeneration patterns for different forest types common to the Northern Forest.
The Acadian Forest of Maine is dominated by mixed tree species, multi-layer forests managed with a range of partial harvesting treatments. Researchers have mapped the tree stems in permanent study plots in managed and unmanaged forest stands of the region and have developed a computer program to estimate a range of distance-dependent competition measures. NSRC researchers will develop new individual tree growth and mortality as well as ingrowth (occurrence, frequency, composition) equations to use in the Acadian Variant of the Forest Vegetation Simulator model. This revised model will help managers more accurately predict future tree growth and regeneration in response to different harvesting scenarios.