Data from: Plant water potential improves prediction of empirical stomatal models

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.气候变化预计将加剧干旱的发生频率与严重程度，对诸多生态系统造成有害影响。气孔对环境变化的响应是所有生态系统模型的核心支撑，但现有模型多基于经验关系构建，且在干旱条件下尚未得到充分验证。本研究依托涵盖全球森林生物群系的34种木本植物数据集，探究叶片水势对气孔导度的影响，并检验3种主流气孔模型与1种新近提出模型的预测精度。研究发现，现有叶片尺度经验模型在干旱条件下，尤其是在土壤水势较低时，存在一致的气孔导度过预测偏差。此外，相较于现有模型，新近提出的气孔导度模型的预测能力有所提升，在干旱条件下的改进尤为显著。本研究结果表明，纳入气孔对水势下降以及由此引发的植物水分运输受损的响应敏感性，可提升干旱条件下的模型预测精度；同时还发现，诸多生物群系中存在多样的植物气孔调控策略，在水分胁迫下从冒险型到保守型不等。要厘清并预测生态系统对未来极端气候事件的响应，亟需实现气孔模拟的此类改进。