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

Citation

Blonder, Benjamin; Escobar, Sabastian; Kapás, Rozália; Michaletz, Sean (2020), Data for: Low predictability of energy balance traits and leaf temperature metrics in desert, montane, and alpine plant communities, Dryad, Dataset, https://doi.org/10.6078/D1NQ59

Abstract

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 1) energy balance traits are only weakly associated with environmental gradients, and 2) are not well predicted by common functional traits. We also show that 3) 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.