Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.
New Phytol. 2018 Nov;220(3):836-850. doi: 10.1111/nph.15333. Epub 2018 Jul 12.
Empirical models of plant drought responses rely on parameters that are difficult to specify a priori. We test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk. We applied four drought treatments to aspen (Populus tremuloides) saplings in a research garden. First we tested the optimization algorithm by using predawn xylem pressure as an input. We then tested the full model which calculates root-zone water budget and xylem pressure hourly throughout the growing season. The optimization algorithm performed well when run from measured predawn pressures. The per cent mean absolute error (MAE) averaged 27.7% for midday xylem pressure, transpiration, net assimilation, leaf temperature, sapflow, diffusive conductance and soil-canopy hydraulic conductance. Average MAE was 31.2% for the same observations when the full model was run from irrigation and rain data. Saplings that died were projected to exceed 85% loss in soil-canopy hydraulic conductance, whereas surviving plants never reached this threshold. The model fit was equivalent to that of an empirical model, but with the advantage that all inputs are specific traits. Prediction is empowered because knowing these traits allows knowing the response to climatic stress.
植物干旱响应的经验模型依赖于难以预先指定的参数。我们测试了一种基于特征和过程的模型，该模型通过优化碳增益与水力风险来预测环境响应。我们在研究花园中对白杨（Populus tremuloides）幼苗进行了四种干旱处理。首先，我们通过使用黎明前木质部压力作为输入来测试优化算法。然后，我们测试了完整的模型，该模型计算了整个生长季节的根区水预算和木质部压力每小时的情况。当从实测的黎明前压力运行时，优化算法的性能良好。对于中午的木质部压力、蒸腾、净同化、叶温、液流、扩散导度和土壤树冠水力导度，平均均方根误差（MAE）为 27.7%。当从灌溉和降雨数据运行完整模型时，相同观测值的平均 MAE 为 31.2%。预计死亡的树苗的土壤树冠水力导度损失将超过 85%，而存活的植物从未达到此阈值。模型拟合与经验模型相当，但具有所有输入都是特定特征的优势。由于了解这些特征可以了解对气候胁迫的响应，因此预测能力得到了增强。
