Data from: Global effects of soil and climate on leaf photosynthetic traits and rates

Aim: The influence of soil properties on photosynthetic traits in higher plants is poorly quantified in comparison with that of climate. We address this situation by quantifying the unique and joint contributions to global leaf-trait variation from soils and climate. Location: Terrestrial ecosystems world-wide. Methods: Using a trait dataset comprising 1509 species from 288 sites, with climate and soil data derived from global datasets, we quantified the effects of 20 soil and 26 climate variables on light-saturated photosynthetic rate (Aarea), stomatal conductance (gs), leaf nitrogen and phosphorus (Narea and Parea) and specific leaf area (SLA) using mixed regression models and multivariate analyses. Results: Soil variables were stronger predictors of leaf traits than climatic variables, except for SLA. On average, Narea, Parea and Aarea increased and SLA decreased with increasing soil pH and with increasing site aridity. gs declined and Parea increased with soil available P (Pavail). Narea was unrelated to total soil N. Joint effects of soil and climate dominated over their unique effects on Narea and Parea, while unique effects of soils dominated for Aarea and gs. Path analysis indicated that variation in Aarea reflected the combined independent influences of Narea and gs, the former promoted by high pH and aridity and the latter by low Pavail. Main conclusions: Three environmental variables were key for explaining variation in leaf traits: soil pH and Pavail, and the climatic moisture index (the ratio of precipitation to potential evapotranspiration). Although the reliability of global soil datasets lags behind that of climate datasets, our results nonetheless provide compelling evidence that both can be jointly used in broad-scale analyses, and that effects uniquely attributable to soil properties are important determinants of leaf photosynthetic traits and rates. A significant future challenge is to better disentangle the covarying physiological, ecological and evolutionary mechanisms that underpin trait–environment relationships.

研究目的：相较于气候因子，当前对土壤属性对高等植物光合性状的影响量化程度仍严重不足。本研究通过量化土壤与气候对全球叶片性状变异的独立贡献与联合贡献，以改善这一研究现状。
研究区域：全球陆地生态系统。
方法：本研究使用涵盖全球288个样地、1509个物种的性状数据集，并结合来自全球数据集的气候与土壤数据，采用混合回归模型与多变量分析方法，量化了20项土壤变量与26项气候变量对光饱和光合速率（Aarea）、气孔导度（gs）、叶片氮含量与磷含量（Narea与Parea）以及比叶面积（SLA）的影响。
结果：除比叶面积（SLA）外，土壤变量对叶片性状的预测能力强于气候变量。平均而言，Narea、Parea与Aarea随土壤pH值与样地干旱程度的升高而上升，SLA则随其升高而下降。气孔导度（gs）随土壤有效磷（Pavail）的增加而降低，Parea则随之升高。Narea与土壤总氮含量无显著关联。土壤与气候的联合效应对Narea与Parea的影响主导于二者的独立效应，而土壤的独立效应对Aarea与gs的影响占据主导地位。通径分析表明，Aarea的变异反映了Narea与gs的独立综合影响：前者受高pH值与高干旱程度的促进，后者则受低土壤有效磷（Pavail）的促进。
主要结论：解释叶片性状变异的三个关键环境变量为：土壤pH值、土壤有效磷（Pavail）以及气候湿度指数（降水量与潜在蒸散量的比值）。尽管全球土壤数据集的可靠性仍落后于气候数据集，但本研究结果仍提供了有力证据，证明二者可联合应用于大尺度分析，且仅由土壤属性介导的效应是叶片光合性状与光合速率的重要决定因素。未来一项重要的研究挑战是，进一步厘清支撑性状-环境关系的协同变异的生理、生态与进化机制。