Clustered disturbances lead to bias in large-scale estimates based on forest sample plots
Clustered disturbances lead to bias in large-scale estimates based on forest sample plots
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DOI：
10.1111/j.1461-0248.2008.01169.x
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发表时间：
2008-06-01
期刊：
影响因子：
8.8
通讯作者：
Chambers, Jeffrey Q.
中科院分区：
文献类型：
--
作者：
Fisher, Jeremy I.;Hurtt, George C.;Chambers, Jeffrey Q.
实地小区评估引导我们目前对全球变化对森林生态系统结构和功能影响的认识。最近对野外样地净碳积累的广泛观察表明，陆地生态系统可能是一个碳汇，可能是气候变化和/或CO2施肥造成的。我们假设现场图可能对固有的罕见死亡事件采样不足，导致当图水平测量扩展到更大的域时出现偏倚。在这项研究中，我们构建了一个简单的计算机模拟模型的森林动态调查的干扰模式的影响，对森林尺度的碳平衡估计。该模型被构造成一个平衡的生物圈在森林生长率的均匀空间格局的尺度。整个景观的干扰间隙大小分布与幂律分布建模。小而频繁的干扰导致混合均匀的异质森林，即使是小的样本地块代表全域的行为。然而，干扰占主导地位的大型和罕见的事件，50公顷的样本地块显示出显着的偏向增长。我们建议，域水平的碳平衡估计样本地块的准确性是高度敏感的整个景观的干扰事件的分布，以及包括估计的实地地块的数量，大小和分布。假设田间小区群可能代表全域条件，应非常谨慎，并保证进一步调查验证。
Assessments from field plots steer much of our current understanding of global change impacts on forest ecosystem structure and function. Recent widespread observations of net carbon accumulation in field plots have suggested that terrestrial ecosystems may be a carbon sink, possibly resulting from climate change and/or CO2 fertilization. We hypothesize that field plots may inadequately sample inherently rare mortality events, leading to bias when plot level measurements are scaled up to larger domains. In this study, we constructed a simple computer simulation model of forest dynamics to investigate the effects of disturbance patterns on landscape-scale carbon balance estimates. The model was constructed to be a balanced biosphere at the landscape-scale with a uniform spatial pattern of forest growth rates. Disturbance gap-size distributions across the landscape were modelled with a power-law distribution. Small and frequent disturbances result in a well-mixed heterogeneous forest where even small sample plots represented domain-wide behaviour. However, with disturbances dominated by large and rare events, sample plots as large as 50 ha displayed significant bias towards growth. We suggest that the accuracy of domain level estimates of carbon balance from sample plots are highly sensitive to the distribution of disturbance events across the landscape, and to the number, size and distribution of field plots that comprise the estimate. Assumptions that small clusters of field plots may be representative of domain-wide conditions should only be made very cautiously, and warrant further investigation for verification.