The influence of sampling design on tree-ring-based quantification of forest growth
The influence of sampling design on tree-ring-based quantification of forest growth
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DOI：
10.1111/gcb.12599
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发表时间：
2014-09-01
影响因子：
11.6
通讯作者：
Frank, David
中科院分区：
文献类型：
--
作者：
Nehrbass-Ahles, Christoph;Babst, Flurin;Frank, David
树木年轮提供了经验性地量化和重建数年至数千年的森林生长动态的少数可能性之一。随着越来越多的科学界采用树木年轮参数，最近的研究表明，常用的抽样设计(即如何以及哪些树木被选作树龄抽样)可能会在量化森林对环境变化的反应方面带来相当大的偏差。到目前为止，还没有对抽样设计对树生态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抽样并重复不同的抽样设计来调查潜在的偏差。实现这一点的方法是，对人口进行追溯性细分，并具体测试气候重建、对气候变化的增长反应、长期增长趋势和森林生产力量化方面出现的偏差。我们发现，通常应用的抽样设计可以给任何类型的基于树轮的调查带来不同程度的系统性偏差，而与所考虑的样本总数无关。森林生长和生产力的量化特别容易受到偏差的影响，而对短期气候变异性的生长反应受抽样设计选择的影响较小。世界上应用最频繁的抽样设计，只关注优势树，可能会使绝对增长率偏差高达459%，趋势偏差超过200%。我们的发现挑战了范式，在范式中，样本的子集通常被认为是代表整个人群的。符合所有类型调查要求的唯一两种抽样战略是：(1)对固定区域内的所有个体进行抽样；(2)完全随机选择树木。这一结果宣传了广泛适用的抽样设计的一致实施，以同时减少基于树轮的森林生长量化的不确定性，并增加超出个别研究、调查人员、实验室和地理边界的数据集的可比性。
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.