Integrating UAV thermal individual-based images and functional traits to investigate thermal sensitivity of Amazonian forestsE

Ongoing global warming threatens to exceed the physiological limits of forests, especially in the tropics, where species operate close to their thermal limits of photosystems. Understanding the relationship between leaf temperature, climatic variables and functional traits is therefore essential to predict the impacts of warming on forest ecology. The climatic safety margins  can be defined as the range of climatic values within which a species in a given environment maintains its physiological functions without risk of severe damage that can ultimately lead to death and are typically computed as the difference between an operational variable of physiological tolerance (e.g. temperature that corresponds to a 50% drop in the quantum photosynthetic efficiency of photosystem II) and a variable of exposure to physiological stress (e.g. maximum leaf temperature). Here, we aim to understand how thermal safety margins vary in response to increased air temperature and water stress at multiple spatial and temporal scales and relate these to species-level functional traits. We present an unprecedented set of surface temperature data measured with Unmanned Aerial Vehicle (UAV) thermal imaging in long-term forest plots in regions subjected to strong seasonal drought and rising temperatures in the southern edge of the Amazon and also for a 20-year drought experiment in northern Amazonia. The data collected includes diurnal patterns of crown temperature collected with UAVs in the dry and wet seasons, climatic variables and functional traits related to thermal and hydraulic tolerance. We examine seasonal variations in canopy temperatures and thermal safety margins and evaluate the extent to which these vary according to canopy structure, leaf size, soil properties and soil moisture availability. Our data provides insights into leaf resilience to warming and factors controlling leaf temperatures in tropical forests. Our results will ultimately help indicate which forest types and species will be better able to cope with future temperature increases.