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.

Usage Notes
photosynthesis_trait_worldwideThe file includes:
A legend sheet with all variables and their description.
A data sheet including trait data (SLA, Nmass, Pmass, Amass, Narea, Parea, Aarea, gs) associated with soil and climate variables which derived (except for annual precipitation and temperature) from worldwide soil and climate datasets
Trait data were collated from sources listed in 'references' sheet and in Wright et al 2004 Nature.globamax_data_160609 (for GEB ms).xlsx

目标：与气候因素相比，土壤性质对高等植物光合特性的影响尚未得到充分量化。为此，本研究通过量化土壤和气候对全球叶片特性变异的独特及联合贡献，旨在解决这一问题。研究地点：全球陆地生态系统。方法：本研究利用包含来自全球288个地点的1509个物种的光合特性数据集，并从全球数据集中提取气候和土壤数据，采用混合回归模型和多变量分析方法，量化了20个土壤变量和26个气候变量对光饱和光合速率（Aarea）、气孔导度（gs）、叶片氮磷含量（Narea和Parea）以及比叶面积（SLA）的影响。结果：土壤变量对叶片特性的预测能力优于气候变量，除SLA外。平均而言，随着土壤pH值和地点干旱度的增加，Narea、Parea和Aarea呈现上升趋势，而SLA则呈下降趋势。随着土壤有效磷（Pavail）的增加，gs下降而Parea上升。Narea与土壤总氮含量无关。土壤和气候的联合效应在Narea和Parea上超过了它们各自的影响，而在Aarea和gs上，土壤的独特效应更为显著。路径分析表明，Aarea的变异反映了Narea和gs的综合独立影响，其中Narea受高pH值和干旱度促进，而gs则受低Pavail的影响。主要结论：三个环境变量是解释叶片特性变异的关键：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落后于气候数据集，但我们的研究结果仍然提供了强有力的证据，表明两者可以共同用于大范围的分析，且土壤性质的独特效应是叶片光合特性和速率的重要决定因素。未来的一大挑战是更好地解析支持特性-环境关系的生理、生态和进化机制，这些机制是共变的。使用说明：光合特性全球数据集。文件包括：所有变量及其描述的图例表；包含与土壤和气候变量相关的特性数据（SLA、Nmass、Pmass、Amass、Narea、Parea、Aarea、gs）的数据表，这些数据来源于全球土壤和气候数据集（除年降水量和温度外）。特性数据收集自“参考文献”表和Wright等（2004）在《Nature》杂志上发表的globamax_data_160609（GEB ms）。xlsx。