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.

中文翻译：

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评估用于复杂和适应性森林管理的林分密度指数的开发和应用

审查目的

林分密度（SD）的客观量化对于预测森林随空间和时间的动态变化是必要的。尽管发展了可持续发展的各种综合表征，但由于数据源、统计建模方法的对比以及森林结构和组成的不同区域差异，关于主要综合措施的共识仍然难以达成。最持久和最有力的 SD 测量方法之一是 Reineke (1933; J. Ag Res. 46, 627-638) 林分密度指数 (SDI)，该指数长期以来已成为预测有关自我疏伐过程的林分发展的基础在单一物种、同龄林分和林分密度管理图 (SDMD) 中。因此，本综述跟踪了正确估计 SDI 的不同方法和必要数据的发展，包括其在复杂森林和适应性管理环境中的应用。

最近的发现

SDI 最初形式的局限性导致了重要的修改，其重点是细化和扩展其应用范围，从同龄、单物种林分扩展到多群体、混合成分林分。更好地确定最大尺寸-密度边界线的统计进展也已应用于 SDI 估算，利用不断扩大的重新测量实地数据（包括大规模国家森林清查）。其他创新包括整合区域气候信息和物种功能特征，例如木材比重、干旱和耐荫性。

概括

在本综述中，我们描述了 SDI 的属性，近 90 年来，这些属性已将其用作 SD 的主要衡量标准。最近应用的稳健统计技术（例如分层贝叶斯方法和线性分位数混合建模）已成为建立最大尺寸-密度边界的最佳方法，特别是那些包含气候等辅助变量的方法。