Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington campus, Loughborough, UK.
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences, University of Warwick, Coventry, UK.
Ann Bot. 2018 Aug 1;122(2):291-302. doi: 10.1093/aob/mcy067.
Diurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the complex ways that photosynthesis responds to changes in light intensity. Current methods to characterize light dynamics, such as ray-tracing, are able to produce data with excellent spatio-temporal resolution but are computationally intensive and the resulting data are complex and high-dimensional. This necessitates development of more economical models for summarizing the data and for simulating realistic light patterns over the course of a day.
High-resolution reconstructions of field-grown plants are assembled in various configurations to form canopies, and a forward ray-tracing algorithm is applied to the canopies to compute light dynamics at high (1 min) temporal resolution. From the ray-tracer output, the sunlit or shaded state for each patch on the plants is determined, and these data are used to develop a novel stochastic model for the sunlit-shaded patterns. The model is designed to be straightforward to fit to data using maximum likelihood estimation, and fast to simulate from.
For a wide range of contrasting 3-D canopies, the stochastic model is able to summarize, and replicate in simulations, key features of the light dynamics. When light patterns simulated from the stochastic model are used as input to a model of photoinhibition, the predicted reduction in carbon gain is similar to that from calculations based on the (extremely costly) ray-tracer data.
The model provides a way to summarize highly complex data in a small number of parameters, and a cost-effective way to simulate realistic light patterns. Simulations from the model will be particularly useful for feeding into larger-scale photosynthesis models for calculating how light dynamics affects the photosynthetic productivity of canopies.
太阳位置和强度的昼夜变化加上植物结构的复杂性导致植物冠层内的光模式具有高度的可变性和动态性。这会通过光合作用对光强度变化的复杂反应方式影响生产力。目前用于描述光动态的方法,如光线追踪,可以生成具有出色时空分辨率的数据,但计算量很大,并且生成的数据复杂且高维。这需要开发更经济的模型来总结数据,并模拟一天中真实的光模式。
以各种配置组装田间生长植物的高分辨率重建体,以形成冠层,并将正向光线追踪算法应用于冠层,以高(1 分钟)时间分辨率计算光动态。从光线追踪器输出中,确定植物上每个斑块的受光或阴影状态,并且这些数据用于开发新颖的用于受光-阴影模式的随机模型。该模型旨在使用最大似然估计直接拟合数据,并且快速模拟。
对于广泛的对比 3-D 冠层,随机模型能够总结并在模拟中复制光动态的关键特征。当从随机模型模拟的光模式用作光抑制模型的输入时,预测的碳增益减少与基于(极其昂贵的)光线追踪器数据的计算相似。
该模型提供了一种在少数参数中总结高度复杂数据的方法,以及一种经济高效的模拟真实光模式的方法。该模型的模拟将特别有助于将更大规模的光合作用模型输入,以计算光动态如何影响冠层的光合作用生产力。