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Tree-ring-based disturbance reconstruction in interdisciplinary research: current state and future directions
Dendrochronologia ( IF 2.7 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.dendro.2020.125733
Jan Altman

Abstract Disturbances play an important role in forest dynamics. The determination of long-term spatiotemporal characteristics of disturbance regimes is essential for understanding forest dynamics and its shifts under global changes. Tree rings are known to provide detailed insight into both temporal and spatial patterns of forest disturbance history. One of the most commonly used indirect tree-ring techniques for investigating past disturbances is growth release detection (GRD), i.e. the abrupt radial growth increase of trees as a reaction to improved light conditions after the death of a disturbed neighbouring canopy tree or trees. However, there are several issues which have not been addressed so far. Here, an overview of GRD and guide for researchers aiming to incorporate GRD into their research is provided, with focus on conventional running mean methods. The aim is to cover various issues of the GRD procedure such as sampling strategy and data quality, selection of appropriate methods and parameter settings, suggested analysis procedures as well as result presentation. Overall, the importance of GRD incorporation in multidisciplinary studies of forest dynamics is highlighted, as it offers a precise tool for gathering long-term information about past disturbances. Lastly, this paper also suggests several future challenges focused on possible utilization of GRD in mainstream ecology to answer long-standing global ecological questions and improve understanding of past processes in forest ecosystems.

中文翻译:

跨学科研究中基于树轮的干扰重建:现状和未来方向

摘要 干扰在森林动态中起着重要作用。确定干扰机制的长期时空特征对于理解森林动态及其在全球变化下的变化至关重要。众所周知,年轮可以提供对森林干扰历史的时间和空间模式的详细洞察。用于调查过去干扰的最常用的间接树轮技术之一是生长释放检测 (GRD),即树木的径向生长突然增加,作为对受干扰的邻近树冠或树木死亡后光照条件改善的反应。然而,有几个问题至今没有得到解决。此处提供了 GRD 概述和旨在将 GRD 纳入其研究的研究人员指南,专注于传统的运行平均方法。目的是涵盖 GRD 程序的各种问题,例如采样策略和数据质量、适当方法和参数设置的选择、建议的分析程序以及结果呈现。总体而言,GRD 在森林动态多学科研究中的重要性得到了强调,因为它提供了一个精确的工具来收集有关过去干扰的长期信息。最后,本文还提出了几个未来的挑战,重点是在主流生态学中可能利用 GRD 来回答长期存在的全球生态问题,并提高对森林生态系统过去过程的理解。选择适当的方法和参数设置、建议的分析程序以及结果展示。总体而言,GRD 在森林动态多学科研究中的重要性得到了强调,因为它提供了一个精确的工具来收集有关过去干扰的长期信息。最后,本文还提出了几个未来的挑战,重点是在主流生态学中可能利用 GRD 来回答长期存在的全球生态问题,并提高对森林生态系统过去过程的理解。选择适当的方法和参数设置、建议的分析程序以及结果展示。总体而言,GRD 在森林动态多学科研究中的重要性得到了强调,因为它提供了一个精确的工具来收集有关过去干扰的长期信息。最后,本文还提出了几个未来的挑战,重点是在主流生态学中可能利用 GRD 来回答长期存在的全球生态问题,并提高对森林生态系统过去过程的理解。
更新日期:2020-10-01
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