Plants exhibit varying strategies for optimizing the trade-off between CO uptake and water loss through transpiration in response to increasing air or soil dryness. Anisohydric plants generally keep their stomata open to maintain or enhance carbon uptake, but this exposes them to a greater risk of hydraulic failure. In contrast, isohydric plants tend to maintain hydraulic integrity by enforcing stricter stomatal and xylem regulation, albeit at the expense of reduced carbon assimilation. Information on the degree of ecosystem isohydricity () is important for predicting plant mortality during drought. However, the current estimation method lacks a physiological basis by not explicitly accounting for the photosynthesis process. Recent advances in observing solar-induced chlorophyll fluorescence (SIF), an effective proxy for photosynthetic rate, provides new potential to estimate at regional and global scales. Based on the revised mechanistic light response (rMLR) model, we developed a mechanistic, SIF-based model of ecosystem-scale isohydricity forced by satellite SIF observations and meteorological datasets. This model was used to estimate daily global ecosystem isohydricity from 2019 to 2020 at a spatial resolution of 0.25°. During the study period, the mean ecosystem isohydricity of global ecosystems was 0.75, suggesting that, on the whole, global terrestrial ecosystems tend to exhibit more anisohydric behavior. Specifically, herbaceous vegetation mostly displaying highly anisohydric behavior, while woody vegetation tended to be more isohydric. Ecosystem isohydricity exhibits clear seasonality, becoming more isohydric in summer. A random-forest analysis revealed that the three most important factors influencing global ecosystem isohydricity are net photosynthesis rate (), canopy height (), and vapor pressure deficit (VPD), which collectively accounted for 86.1% of the importance. We also found that ecosystem isohydricity reflects a plant-environment interaction, with the contribution of intrinsic hydraulic traits likely exceeding 50%. The proposed model enables us to incorporate plant physiological controls into the estimation of ecosystem-scale , thereby enhancing our ability to track plant water-use strategies in response to rising water stress in a changing climate.

中文翻译：

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根据太阳引起的叶绿素荧光和气象数据集估计全球生态系统等水性

植物表现出不同的策略，通过蒸腾作用来优化二氧化碳吸收和水分流失之间的权衡，以应对空气或土壤干燥度的增加。不等水植物通常保持气孔打开以维持或增强碳吸收，但这使它们面临更大的水力衰竭风险。相比之下，等水植物倾向于通过实施更严格的气孔和木质部调节来维持水力完整性，尽管是以减少碳同化为代价的。有关生态系统等水性程度的信息对于预测干旱期间植物的死亡率非常重要。然而，目前的估计方法缺乏生理基础，没有明确考虑光合作用过程。太阳诱导叶绿素荧光（SIF）是光合速率的有效代表，观测方面的最新进展为区域和全球尺度的估计提供了新的潜力。基于修订后的机械光响应 (rMLR) 模型，我们开发了一种基于 SIF 的机械模型，该模型由卫星 SIF 观测和气象数据集强制实现生态系统规模等水性。该模型用于估算2019年至2020年每日全球生态系统等水性，空间分辨率为0.25°。研究期间，全球生态系统的平均生态系统等水度为0.75，表明总体上全球陆地生态系统往往表现出更多的不等水行为。具体来说，草本植被大多表现出高度不等水行为，而木本植被则倾向于更加等水。生态系统等水性表现出明显的季节性，在夏季变得更加等水性。随机森林分析表明，影响全球生态系统等水性的三个最重要因素是净光合作用速率（）、冠层高度（）和水汽压赤字（VPD），合计占重要性的86.1%。我们还发现，生态系统等水性反映了植物与环境的相互作用，内在水力特征的贡献可能超过 50%。所提出的模型使我们能够将植物生理控制纳入生态系统规模的估计中，从而增强我们跟踪植物用水策略以应对气候变化中不断上升的水压力的能力。