Department of Earth and Environment, Boston University, 675 Commonwealth Ave, Rm 130, Boston, MA, 02215, USA,
Photosynth Res. 2014 Feb;119(1-2):3-14. doi: 10.1007/s11120-013-9836-z. Epub 2013 May 5.
Regional and global models of the terrestrial biosphere depend critically on models of photosynthesis when predicting impacts of global change. This paper focuses on identifying the primary data needs of these models, what scales drive uncertainty, and how to improve measurements. Overall, there is a need for an open, cross-discipline database on leaf-level photosynthesis in general, and response curves in particular. The parameters in photosynthetic models are not constant through time, space, or canopy position but there is a need for a better understanding of whether relationships with drivers, such as leaf nitrogen, are themselves scale dependent. Across time scales, as ecosystem models become more sophisticated in their representations of succession they needs to be able to approximate sunfleck responses to capture understory growth and survival. At both high and low latitudes, photosynthetic data are inadequate in general and there is a particular need to better understand thermal acclimation. Simple models of acclimation suggest that shifts in optimal temperature are important. However, there is little advantage to synoptic-scale responses and circadian rhythms may be more beneficial than acclimation over shorter timescales. At high latitudes, there is a need for a better understanding of low-temperature photosynthetic limits, while at low latitudes the need is for a better understanding of phosphorus limitations on photosynthesis. In terms of sampling, measuring multivariate photosynthetic response surfaces are potentially more efficient and more accurate than traditional univariate response curves. Finally, there is a need for greater community involvement in model validation and model-data synthesis.
当预测全球变化的影响时，陆地生物圈的区域和全球模型严重依赖于光合作用模型。本文的重点是确定这些模型的主要数据需求、驱动不确定性的尺度以及如何改进测量。总的来说，需要建立一个开放的、跨学科的叶片水平光合作用数据库，特别是对响应曲线的研究。光合作用模型中的参数不是随时间、空间或冠层位置而保持不变的，但需要更好地了解与叶片氮等驱动因素的关系本身是否也具有尺度依赖性。在时间尺度上，随着生态系统模型在演替方面的表现更加复杂，它们需要能够近似光斑响应，以捕捉林下生长和存活。在高纬度和低纬度地区，光合作用数据总体上都不足，特别是需要更好地了解热驯化。简单的驯化模型表明，最佳温度的变化很重要。然而，在更长的时间尺度上，生理节奏可能比驯化更有利，而不是对天气尺度的响应。在高纬度地区，需要更好地了解低温光合作用的限制，而在低纬度地区，需要更好地了解光合作用对磷的限制。在采样方面，测量多变量光合作用响应曲面比传统的单变量响应曲线更有效和更准确。最后，需要更多的社区参与模型验证和模型数据综合。
