Climate fails to predict wood decomposition at regional scales
Climate fails to predict wood decomposition at regional scales
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DOI：
10.1038/nclimate2251
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发表时间：
2014-07-01
影响因子：
30.7
通讯作者：
King, Joshua R.
中科院分区：
文献类型：
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作者：
Bradford, Mark A.;Warren, Robert J., II;King, Joshua R.
有机物的分解强烈影响生态系统碳储量（1）。在地球系统模型中，气候是有机物分解速率的主要控制因素（2-5）。这一假设是基于分解对气候的平均响应，但在全球变化科学的其他领域，人们越来越认识到，基于平均响应的预测可能是不相关的和误导性的（6，7）。我们测试了气候对枯木分解率的控制-估计全球碳储量为73 +/- 6 Pg碳（8）-是否对推断它们的空间尺度敏感。我们发现，只有当局部尺度的变化被聚合成平均值时，气候是分解的主要控制的共同假设才得到支持。分类数据显示，当地规模的因素解释了73%的木材分解的变化，气候只有28%。此外，从局部与平均值分析估计的分解的温度敏感性是1.3倍。平均相关性的基本问题在几十年前就被强调了（9，10），但平均气候分解关系被用来生成模拟，为环境变化下的管理和适应提供信息。我们的研究结果表明，要准确预测分解将如何应对气候变化，模型必须考虑到控制区域动态的局部尺度因素。
Decomposition of organic matter strongly influences ecosystem carbon storage(1). In Earth-system models, climate is a predominant control on the decomposition rates of organic matter(2-5). This assumption is based on the mean response of decomposition to climate, yet there is a growing appreciation in other areas of global change science that projections based on mean responses can be irrelevant and misleading(6,7). We test whether climate controls on the decomposition rate of dead wood-a carbon stock estimated to represent 73 +/- 6 Pg carbon globally(8)-are sensitive to the spatial scale from which they are inferred. We show that the common assumption that climate is a predominant control on decomposition is supported only when local-scale variation is aggregated into mean values. Disaggregated data instead reveal that local-scale factors explain 73% of the variation in wood decomposition, and climate only 28%. Further, the temperature sensitivity of decomposition estimated from local versus mean analyses is 1.3-times greater. Fundamental issues with mean correlations were highlighted decades ago(9,10), yet mean climate-decomposition relationships are used to generate simulations that inform management and adaptation under environmental change. Our results suggest that to predict accurately how decomposition will respond to climate change, models must account for local-scale factors that control regional dynamics.