Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia.
Plant Physiology Division, Coconut Research Institute of Sri Lanka, Lunuwila, 61150, Sri Lanka.
New Phytol. 2019 Apr;222(2):768-784. doi: 10.1111/nph.15668. Epub 2019 Feb 8.
The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO response curves, including data from 141 C species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common-garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation than adaptation to temperature at climate of origin. We developed a summary model to represent photosynthetic temperature responses and showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate.
光合作用对温度的响应是决定全球植被模型(GVM)对变暖预测响应的关键因素之一。由于对气候的遗传适应,这种响应可能在地理上有所不同,而且由于对环境温度变化的适应,这种响应也可能随时间而变化。我们的目标是开发一个稳健的定量全球模型,以代表光合作用温度响应的驯化和适应。我们使用包括来自热带雨林到北极苔原的 141 种 C 种的光合作用 CO 响应曲线的全球数据集,量化并模拟了负责光合作用温度驯化和适应的关键机制,包括数据。我们通过分别考虑季节性和共同花园数据集来分离温度驯化和适应过程。光合作用最适温度的全球变化主要归因于光合作用的生化限制,而不是气孔导度或呼吸作用。我们发现,与起源气候的温度适应相比,生长温度的驯化是导致这种变化的更强驱动力。我们开发了一个综合模型来表示光合作用的温度响应,并表明它可以高精度地预测最优温度的全球变化。这种新的算法应该能够提高对全球生态系统在变暖气候下功能的预测。