Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area
Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area
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DOI：
10.1111/1365-2435.12832
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发表时间：
2017-06
影响因子：
5.2
通讯作者：
S. Smart;H. Glanville;M. C. Blanes;L. Mercado;B. Emmett;David L. Jones;B. Cosby;R. Marrs;A. Butler;M. Marshall;S. Reinsch;C. Herrero-Jáuregui;J. Hodgson
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文献类型：
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作者：
S. Smart;H. Glanville;M. C. Blanes;L. Mercado;B. Emmett;David L. Jones;B. Cosby;R. Marrs;A. Butler;M. Marshall;S. Reinsch;C. Herrero-Jáuregui;J. Hodgson
1.以高分辨率对地上净初级生产力（aNPP）进行可靠建模是一项重大挑战。改进过程模型的一个有前途的途径是包括反应和效应特质关系。然而，仍然存在不确定性，叶片性状与aNPP的相关性最强。2.我们比较了两个最广泛使用的性状从叶经济谱（比叶面积和叶干物质含量）的丰度加权值与跨温带生态系统梯度测量aNPP。3.我们发现，叶干物质含量（LDMC），而不是比叶面积（SLA）是上级预测aNPP（R 2 =0.55）。4.直接测量的优势种原位性状值显着提高了aNPP的估计。引入种内性状变异，包括从公布的数据库复制性状值的影响，并没有提高aNPP的估计。5.我们的研究结果支持了以更少的成本获得更大的科学理解的前景，因为LDMC比SLA更容易测量。
1. Reliable modelling of above-ground Net Primary Production (aNPP) at fine resolution is a significant challenge. A promising avenue for improving process models is to include response and effect trait relationships. However, uncertainties remain over which leaf traits are correlated most strongly with aNPP. 2. We compared abundance-weighted values of two of the most widely used traits from the Leaf Economics Spectrum (Specific Leaf Area and Leaf Dry Matter Content) with measured aNPP across a temperate ecosystem gradient. 3. We found that Leaf Dry Matter Content (LDMC) as opposed to Specific Leaf Area (SLA) was the superior predictor of aNPP (R2=0.55). 4. Directly measured in situ trait values for the dominant species improved estimation of aNPP significantly. Introducing intra-specific trait variation by including the effect of replicated trait values from published databases did not improve the estimation of aNPP. 5. Our results support the prospect of greater scientific understanding for less cost because LDMC is much easier to measure than SLA.