Is analysing the nitrogen use at the plant canopy level a matter of choosing the right optimization criterion?
Is analysing the nitrogen use at the plant canopy level a matter of choosing the right optimization criterion?
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DOI：
10.1007/s00442-011-2011-3
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发表时间：
2011-10
期刊：
影响因子：
2.7
通讯作者：
During, Heinjo J.
中科院分区：
文献类型：
--
作者：
Anten, Niels P. R.;During, Heinjo J.
关键词：
优化理论与冠层模型相结合，是评价光合作用相关植物性状自适应意义的有力工具。然而，由于对适当的优化准则缺乏共识，它的成功应用受到了阻碍。在这里，我们回顾了基于不同类型优化标准的模型如何用于分析决定冠层水平光合氮利用效率的性状，特别是氮再分配和叶面积指数。到目前为止，最常用的方法是静态工厂简单优化（SSO）。静态植物简单优化有两个假设：(1)忽略个体间的竞争相互作用，当植物性状最大化时，被认为是最优的；(2)假设植物是静态的，忽略了冠层动态（叶片的产生和损失、氮的再分配和吸收）和非光合组织的呼吸作用。近年来的研究通过应用进化博弈论（evolutionary game theory， EGT）或应用动态植物简单优化（dynamic-plant simple optimization， DSO）解决了前者的问题，并在对植物光合特性的认识方面取得了长足的进展。然而，我们认为未来的模型研究应侧重于将这两种方法结合起来。我们还指出，现场观测可以拟合基于非常不同的优化准则的两种模型的预测。因此，为了进一步了解光合作用相关植物性状的适应性意义，迫切需要进行实验来检验潜在的优化标准和关于优化潜在机制的竞争性假设。
Optimization theory in combination with canopy modeling is potentially a powerful tool for evaluating the adaptive significance of photosynthesis-related plant traits. Yet its successful application has been hampered by a lack of agreement on the appropriate optimization criterion. Here we review how models based on different types of optimization criteria have been used to analyze traits—particularly N reallocation and leaf area indices—that determine photosynthetic nitrogen-use efficiency at the canopy level. By far the most commonly used approach is static-plant simple optimization (SSO). Static-plant simple optimization makes two assumptions: (1) plant traits are considered to be optimal when they maximize whole-stand daily photosynthesis, ignoring competitive interactions between individuals; (2) it assumes static plants, ignoring canopy dynamics (production and loss of leaves, and the reallocation and uptake of nitrogen) and the respiration of nonphotosynthetic tissue. Recent studies have addressed either the former problem through the application of evolutionary game theory (EGT) or the latter by applying dynamic-plant simple optimization (DSO), and have made considerable progress in our understanding of plant photosynthetic traits. However, we argue that future model studies should focus on combining these two approaches. We also point out that field observations can fit predictions from two models based on very different optimization criteria. In order to enhance our understanding of the adaptive significance of photosynthesis-related plant traits, there is thus an urgent need for experiments that test underlying optimization criteria and competing hypotheses about underlying mechanisms of optimization.
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