Fast, accurate and accessible calculations of leaf temperature and its physiological consequences with NicheMapR.

Mechanistic predictions of ecological niches are fundamentally based on energy and mass exchange processes that, to be realistic, must be linked to microclimate and incorporate behaviour and physiology. Estimates of leaf temperature and associated transpiration and net photosynthesis rates are fundamental in ecology, agriculture and global change biology. Equations for calculating leaf energy budgets have been available for over 60 years and complex models of stomatal behaviour have been developed. However, user‐friendly ways of estimating leaf temperature under realistic microclimatic regimes, including soil moisture effects, remain limited.Here we show that the integrated microclimate and ectotherm functions of the NicheMapR package for the R programming environment can be used to make fast and effective estimates of leaf temperature and associated rates of water loss and photosynthesis for a wide range of leaf functional types, root depths and microhabitats. Stomatal conductance may be constant or simulated to change dynamically (a) as a function of vapour pressure deficit and CO2 concentration, (b) as a function of soil water potential and (c) as a thermoregulatory mechanism to avoid heat stress.We tested the approach against hourly leaf temperature observations in summer from an arid shrubland in Australia, a temperate woodland in Montana USA, along an elevational gradient in Colorado USA, and at a botanical garden in Florida, USA.National or global gridded data sets can provide the required forcing data but local observational data or some combination of local and gridded data can be used. The global ERA5 and NCEP datasets allow historical leaf energy budget calculations to be made anywhere in the world and a Shiny app has been developed to facilitate use of the model with these datasets.Situated leaf energy budget calculations can be used to design informative field programs to collect much needed detailed temporal data on leaf temperature under natural settings, enabling better predictions and interpretations of how plants respond to environmental change. [ABSTRACT FROM AUTHOR]

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