Leaf and fine root carbon stocks and turnover are coupled across Arctic ecosystems.
Leaf and fine root carbon stocks and turnover are coupled across Arctic ecosystems.
复制标题
北极生态系统中的叶子和细根碳储量和周转率是相互关联的。
DOI：
10.1111/gcb.12322
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发表时间：
2013
影响因子：
11.6
通讯作者：
Sloan VL
中科院分区：
文献类型：
--
作者：
Sloan VL
估算植被碳库及其周转率对于理解和模拟生态系统对气候变化的反应及其对气候的反馈至关重要。在北极，一个地区包含全球重要的土壤碳储存，并预计在未来世纪最迅速的气候变化，植物群落平均有六倍以上的生物量地下比地上，但知识的根碳库的大小和周转率是有限的。研究结果表明，在8个群落中，叶片和细根周转率之间存在显著的正相关关系（r2= 0.68，P <0.05），叶片（r2= 0.63，P <0.05）和细根（r2= 0.55，P <0.05）周转率与叶面积指数（LAI，单位面积叶面积）均呈显著正相关。这种根和叶动态的耦合支持了整个植物经济学谱的理论。我们还表明，细根碳库的大小最初随LAI的增加而线性增加，然后在LAI = 1 m2m−2时趋于平稳，表明在整个群落尺度上叶和细根投资之间存在功能平衡。这些生态关系不仅证明了地上和地下植物碳动态之间的密切联系，而且还允许从LAI的单个容易量化（和遥感）参数预测植物碳库大小及其周转率，包括估计卫星根系数据的可能性。
Estimates of vegetation carbon pools and their turnover rates are central to understanding and modelling ecosystem responses to climate change and their feedbacks to climate. In the Arctic, a region containing globally important stores of soil carbon, and where the most rapid climate change is expected over the coming century, plant communities have on average sixfold more biomass below ground than above ground, but knowledge of the root carbon pool sizes and turnover rates is limited. Here, we show that across eight plant communities, there is a significant positive relationship between leaf and fine root turnover rates (r2= 0.68,P<0.05), and that the turnover rates of both leaf (r2= 0.63,P<0.05) and fine root (r2= 0.55,P<0.05) pools are strongly correlated with leaf area index (LAI, leaf area per unit ground area). This coupling of root and leaf dynamics supports the theory of a whole‐plant economics spectrum. We also show that the size of the fine root carbon pool initially increases linearly with increasing LAI, and then levels off at LAI = 1 m2m−2, suggesting a functional balance between investment in leaves and fine roots at the whole community scale. These ecological relationships not only demonstrate close links between above and below‐ground plant carbon dynamics but also allow plant carbon pool sizes and their turnover rates to be predicted from the single readily quantifiable (and remotely sensed) parameter of LAI, including the possibility of estimating root data from satellites.