Our Approach & the SCN

Structural Complexity Network (SCN)

The Structural Complexity Network is a network of colleagues sharing an interest in three-dimensional forest stand structure.   Each participant has collaborated with our lab to explore the spatial patterns of structure in stands for which they have stem-mapped data available.   Our goal is to better understand the range of natural and potential structural complexity values for temperate forests of various types.

Currently the SCN includes stands from Oregon, Washington, Minnesota, Pennsylvania, Finland, Iran, Slovenia, and Switzerland, but data have already been collected to add stands in Maryland, Ohio, West Virginia, Vermont, and the Ukraine.  If you have stem-mapped data and would like to join the network, just email Eric Zenner.

Spatially Explicit Tree-based Forest Structure

Processes such as regeneration and competition occur at the tree-level with interactions occurring within tree neighborhoods.  These neighborhoods can be mathematically delineated as polygons, encompassing all the surrounding trees with which a tree can be connected with a straight line without crossing any other lines.


Structure can be explored by looking at the size differential of trees in a neighborhood:  when the trees within a neighborhood are relatively similar in size, the mapped surface connecting them has a shallow slope (here in cool colors like blue), whereas the slope is steeper when there is a larger difference in size among the trees (here in warm colors like orange).  Calculated over the entire stem-mapped area (such as the 1 ha stand below), a measure of 3-D (x, y, and size) structural heterogeneity can be calculated, known as the Structural Complexity Index (or SCI).


The SCI can be applied to any stem-mapped stand and factors other than size can be used for the third dimension (e.g., z=monetary or habitat value).  The SCI is but one of many metrics and indices used to characterize forest structure in stands within the SCN, but is very promising because it can capture both horizontal (e.g., spatial pattern) and vertical (e.g., tree size) heterogeneity, is relatively scale-invariant, and can be flexibly tailored to desired ecosystem services.

Zenner, E.K. and D.E. Hibbs.  2000.  A new method for modeling the heterogeneity of forest structure.  Forest Ecology and Management 129:75-87.