Swiss Federal Research Institute WSL, Zürcherstrasse 111, Birmensdorf, CH-8903, Switzerland.
Glob Chang Biol. 2014 Sep;20(9):2867-85. doi: 10.1111/gcb.12599. Epub 2014 Jun 11.
Tree-rings offer one of the few possibilities to empirically quantify and reconstruct forest growth dynamics over years to millennia. Contemporaneously with the growing scientific community employing tree-ring parameters, recent research has suggested that commonly applied sampling designs (i.e. how and which trees are selected for dendrochronological sampling) may introduce considerable biases in quantifications of forest responses to environmental change. To date, a systematic assessment of the consequences of sampling design on dendroecological and-climatological conclusions has not yet been performed. Here, we investigate potential biases by sampling a large population of trees and replicating diverse sampling designs. This is achieved by retroactively subsetting the population and specifically testing for biases emerging for climate reconstruction, growth response to climate variability, long-term growth trends, and quantification of forest productivity. We find that commonly applied sampling designs can impart systematic biases of varying magnitude to any type of tree-ring-based investigations, independent of the total number of samples considered. Quantifications of forest growth and productivity are particularly susceptible to biases, whereas growth responses to short-term climate variability are less affected by the choice of sampling design. The world's most frequently applied sampling design, focusing on dominant trees only, can bias absolute growth rates by up to 459% and trends in excess of 200%. Our findings challenge paradigms, where a subset of samples is typically considered to be representative for the entire population. The only two sampling strategies meeting the requirements for all types of investigations are the (i) sampling of all individuals within a fixed area; and (ii) fully randomized selection of trees. This result advertises the consistent implementation of a widely applicable sampling design to simultaneously reduce uncertainties in tree-ring-based quantifications of forest growth and increase the comparability of datasets beyond individual studies, investigators, laboratories, and geographical boundaries.
树木年轮为量化和重建多年至千年的森林生长动态提供了为数不多的可能性之一。随着越来越多的科学界人士采用树木年轮参数，最近的研究表明，通常应用的采样设计（即如何以及选择哪些树木进行树木年代学采样）可能会在量化森林对环境变化的响应方面引入相当大的偏差。迄今为止，尚未对采样设计对树木年代学和气候学结论的影响进行系统评估。在这里，我们通过对大量树木进行采样并复制各种采样设计来研究潜在的偏差。这是通过回溯抽样来实现的，具体来说，我们测试了气候重建、对气候变化的生长响应、长期生长趋势和森林生产力量化方面出现的偏差。我们发现，无论考虑的样本总数如何，通常应用的采样设计都会对任何类型的基于树木年轮的研究产生系统的、大小不一的偏差。森林生长和生产力的量化特别容易受到偏差的影响，而对短期气候变化的生长响应受采样设计选择的影响较小。世界上最常应用的采样设计仅关注优势树种，会使绝对生长率产生高达 459%的偏差，并且趋势偏差超过 200%。我们的研究结果挑战了一种范例，即通常认为样本的子集代表整个种群。只有两种采样策略满足所有类型调查的要求：（i）在固定区域内对所有个体进行采样；（ii）随机选择树木。这一结果提倡实施一种广泛适用的采样设计，以同时减少基于树木年轮的森林生长量化的不确定性，并增加数据集在单个研究、调查人员、实验室和地理边界之外的可比性。
