Division of Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
PLoS One. 2012;7(5):e37914. doi: 10.1371/journal.pone.0037914. Epub 2012 May 23.
Nitrogen is a dominant regulator of vegetation dynamics, net primary production, and terrestrial carbon cycles; however, most ecosystem models use a rather simplistic relationship between leaf nitrogen content and photosynthetic capacity. Such an approach does not consider how patterns of nitrogen allocation may change with differences in light intensity, growing-season temperature and CO(2) concentration. To account for this known variability in nitrogen-photosynthesis relationships, we develop a mechanistic nitrogen allocation model based on a trade-off of nitrogen allocated between growth and storage, and an optimization of nitrogen allocated among light capture, electron transport, carboxylation, and respiration. The developed model is able to predict the acclimation of photosynthetic capacity to changes in CO(2) concentration, temperature, and radiation when evaluated against published data of V(c,max) (maximum carboxylation rate) and J(max) (maximum electron transport rate). A sensitivity analysis of the model for herbaceous plants, deciduous and evergreen trees implies that elevated CO(2) concentrations lead to lower allocation of nitrogen to carboxylation but higher allocation to storage. Higher growing-season temperatures cause lower allocation of nitrogen to carboxylation, due to higher nitrogen requirements for light capture pigments and for storage. Lower levels of radiation have a much stronger effect on allocation of nitrogen to carboxylation for herbaceous plants than for trees, resulting from higher nitrogen requirements for light capture for herbaceous plants. As far as we know, this is the first model of complete nitrogen allocation that simultaneously considers nitrogen allocation to light capture, electron transport, carboxylation, respiration and storage, and the responses of each to altered environmental conditions. We expect this model could potentially improve our confidence in simulations of carbon-nitrogen interactions and the vegetation feedbacks to climate in Earth system models.
氮是植被动态、净初级生产力和陆地碳循环的主要调节因子；然而，大多数生态系统模型使用的是叶片氮含量与光合作用能力之间相当简单的关系。这种方法没有考虑到氮分配的模式可能随着光照强度、生长季节温度和 CO2 浓度的差异而发生变化。为了考虑到氮-光合作用关系中的这种已知可变性，我们基于氮在生长和储存之间的分配权衡以及氮在光捕获、电子传递、羧化和呼吸之间的分配优化，开发了一种机制性氮分配模型。当根据已发表的 V(c,max)（最大羧化速率）和 J(max)（最大电子传递速率）数据评估时，所开发的模型能够预测光合作用能力对 CO2 浓度、温度和辐射变化的适应。对草本植物、落叶树和常绿树的模型进行敏感性分析表明，高浓度的 CO2 会导致氮向羧化作用的分配减少，但向储存的分配增加。较高的生长季节温度会导致氮向羧化作用的分配减少，这是由于光捕获色素和储存所需的氮增加。较低的辐射水平对草本植物的氮向羧化作用的分配影响比树木更大，这是由于草本植物对光捕获的氮需求更高。据我们所知，这是第一个同时考虑氮向光捕获、电子传递、羧化、呼吸和储存分配以及每个分配对环境变化的响应的完整氮分配模型。我们希望该模型可以提高我们对地球系统模型中碳氮相互作用和植被对气候反馈的模拟的信心。
