Developing a Structural Complexity Index for Oriental Beech Forests in Northern Iran

Abstract

Abstract The structural complexity index (SCI) has become an important metric for forest managers to monitor ecosystem services and conservation value in a wide variety of forest types. In this study, we developed an SCI for an unmanaged mixed Fagus orientalis Lipsky forest in northern Iran, which incorporated structural information specific to mature and old-growth forests. Our results showed that we were able to develop an SCI for this forest that would assist managers to make conservation decisions in a forest where large overstory trees and small understory trees are equally important. The SCI was significantly positively correlated with the density of five minor tree species (Acer velutinum Boiss., Acer cappadocicum Gled., Tilia begoniifolia Chun & H.D. Wong, Quercus castaneifolia C.A. Mey., and Prunus avium L.) and the density of large ([50 cm < diameter at breast height {DBH} < 75 cm]) and very large (DBH > 75 cm) overstory trees. The SCI remains a highly flexible tool for forest conservation and decision making and may assist with decisions about forest management in response to climate change and shifting disturbance regimes. Study Implications: Forest managers have begun to use the structural complexity index (SCI) to assess the successful achievement of conservation management objectives. In many mature, mixed-species forests, large trees, minor species, and understory tree species are important structural components. In this study, we demonstrate that reduction of the minimum diameter used for sampling trees and careful selection of the variables used to calculate SCI results in a valuable metric for making conservation decisions. The advantage of SCI as a forest decision tool is that forest managers are able to adjust the inputs used to calculate SCI to reflect specific management objectives or monitoring goals.