Low predictability of energy balance traits and leaf temperature metrics in desert, montane and alpine plant communities,Functional Ecology

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Low predictability of energy balance traits and leaf temperature metrics in desert, montane and alpine plant communities
Functional Ecology ( IF 4.6 ) Pub Date : 2020-08-21 , DOI: 10.1111/1365-2435.13643
Benjamin Blonder _{1,

2,

3} , Sabastian Escobar ₁ , Rozália E. Kapás _{1,

4} , Sean T. Michaletz _{1,

5,

6}

Affiliation

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, T_leaf − T_air difference) or thermal coupling strengths (β, T_leaf vs. T_air slope) is challenging.
We ask: (a) whether environmental gradients predict variation in energy balance traits (absorptance, leaf angle, stomatal distribution, maximum stomatal conductance, leaf area, leaf height); (b) whether commonly measured leaf functional traits (dry matter content, mass per area, nitrogen fraction, δ¹³C, height above ground) predict energy balance traits; and (c) how traits and environmental variables predict δ and β among species.
We address these questions with diurnal measurements of 41 species co‐occurring along a 1,100 m elevation gradient spanning desert to alpine biomes. We show that (a) energy balance traits are only weakly associated with environmental gradients and (b) are not well predicted by common functional traits. We also show that (c) δ and β can be partially approximated using interactions among site environment and traits, with a much larger role for environment than traits. The heterogeneity in leaf temperature metrics and energy balance traits challenges larger‐scale predictive models of plant performance under environmental change.

中文翻译：

沙漠，山地和高山植物群落中能量平衡性状和叶片温度指标的可预测性较低

叶片能量平衡可能会影响植物的生长性能和群落组成。虽然生物物理理论可以将叶片能量平衡与许多性状和环境变量联系起来，但是预测叶温和关键驾驶员性状的参数设置不完全仍然具有挑战性。预测的热偏移（δ，Ť_叶 - Ť_空气差）或热耦合强度（β，Ť_叶与Ť_空气斜率）是具有挑战性的。
我们问：（a）环境梯度是否可以预测能量平衡性状的变化（吸收率，叶片角度，气孔分布，最大气孔导度，叶片面积，叶片高度）；（b）中常用的测量叶功能性状（干物质含量，单位面积，氮馏分质量，δ是否¹³ C，离地高度）预测能量平衡性状; （c）性状和环境变量如何预测物种之间的δ和β。
我们通过对从沙漠到高山生物群落的1,100 m海拔梯度同时发生的41种物种进行昼夜测量来解决这些问题。我们表明（a）能量平衡特征仅与环境梯度弱相关，并且（b）不能由常用功能特征很好地预测。我们还表明，（c）δ和β可以使用站点环境和性状之间的相互作用来部分近似，而环境对环境的作用要比性状大得多。叶片温度指标和能量平衡特征的异质性挑战了环境变化下植物性能的大规模预测模型。

更新日期：2020-08-21

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