Evaluating the Development and Application of Stand Density Index for the Management of Complex and Adaptive Forests

Purpose of Review

The objective quantification of stand density (SD) is necessary for predicting forest dynamics over space and time. Despite the development of various synthetic representations of SD, consensus remains elusive regarding a primary integrated measure due to contrasting data sources, statistical modeling methods, and distinct regional variations in forest structure and composition. One of the most enduring and robust measures of SD is Reineke’s (1933; J. Ag Res. 46, 627-638) stand density index (SDI), which has long formed the basis for the prediction of stand development concerning self-thinning processes in single-species, even-aged stands and stand density management diagrams (SDMDs). Thus, this review tracks the development of different methodologies and necessary data for properly estimating SDI, including its application in complex forests and adaptive management contexts.

Recent Findings

Limitations of SDI in its earliest form have led to important modifications centered on refinement and expanding its application beyond even-aged, single-species stands to multi-cohort, mixed composition stands. Statistical advances for better determination of the maximum size-density boundary line have also been applied to SDI estimates using the ever-expanding availability of remeasured field data including large-scale, national forest inventories. Other innovations include the integration of regional climate information and species functional traits, e.g., wood specific gravity, drought, and shade tolerance.

Summary

In this synthesis, we describe the attributes of SDI that have promulgated its use as a leading measure of SD for nearly 90 years. Recent applications of robust statistical techniques such as hierarchical Bayesian methods and linear quantile mixed modeling have emerged as the best performing methods for establishing the maximum size-density boundary, especially those incorporating ancillary variables like climate.