Data for: Low predictability of energy balance traits and leaf temperature metrics in desert, montane, and alpine plant communities

Leaf energy balance may influence plant performance and community composition. While biophysical theory can link leaf energy balance to many traits and environment variables, predicting leaf temperature and key driver traits with incomplete parameterizations remains challenging. Predicting thermal offsets (δ, Tleaf – Tair difference) or thermal coupling strengths (β, Tleaf vs. Tair slope) is challenging. We ask: 1) whether environmental gradients predict variation in energy balance traits (absorptance, leaf angle, stomatal distribution, maximum stomatal conductance, leaf area, leaf height); 2) whether commonly-measured leaf functional traits (dry matter content, mass per area, nitrogen fraction, δ13C, height above ground) predict energy balance traits; and 3) how traits and environmental variables predict δ and β among species. We address these questions with diurnal measurements of 41 species co-occurring along an 1100 m elevation gradient spanning desert to alpine biomes. We show that...

叶片能量平衡（leaf energy balance）可影响植物生长表现与群落组成。尽管生物物理理论能够将叶片能量平衡与众多功能性状及环境变量构建关联，但在参数化方案不完备的前提下，预测叶片温度及其关键驱动性状仍颇具挑战。预测热偏移量（thermal offsets，δ，即叶片温度与气温的差值）或热耦合强度（thermal coupling strengths，β，即叶片温度与气温的回归斜率）同样颇具难度。本研究旨在解答以下三个问题：1）环境梯度能否预测能量平衡性状（energy balance traits，包括叶片吸收率、叶角度、气孔分布、最大气孔导度、叶面积、叶高）的变异？2）常规测定的叶片功能性状（leaf functional traits，包括干物质含量、比叶质量、氮组分、δ¹³C、地上高度）能否预测能量平衡性状？3）物种间的性状与环境变量如何共同预测热偏移量δ与热耦合强度β？我们依托沿1100米海拔梯度分布、覆盖从荒漠到高山生物群区的41个同域分布物种的日间观测数据解答上述问题，研究结果表明……