The Amazon is the most important biome of South America, harbouring extraordinarily high levels of biodiversity and providing important ecosystems services. This biome is particularly notable for evolving independently from fire and in a moist, warm climate. In recent decades, altered fire regimes and an increasingly hotter and drier climate has pushed this key biome towards ecological thresholds that will likely lead to major losses in biodiversity and ecosystem services. Similarly, the ecotonal forests at the Amazon-Cerrado transition are unique ecosystems in terms of form and function, but they may be the first to suffer large-scale tree mortality and species loss due to the combined effects of increased anthropogenic disturbance, altered fire regimes and a drier climate. 

Vulnerability of fire and droughts are closely intertwined in Amazonian and transitional forests because fires in this region only occur when there is water stress and a human ignition source. Thus, drought increases vulnerability to fire, but we do not yet understand the magnitude and spatial variation of these vulnerabilities. Once a forest burns there is immediate tree mortality, but recent evidence also shows a significant time-lagged mortality that can last for decades, becoming an important carbon source. However, the mechanistic processes that lead to time-lagged tree mortality in this myriad of forest ecosystems encompassing the Amazon biome and the Amazon-Cerrado transition are still poorly understood. We also lack knowledge on how these processes might vary spatially across the biome and its transition. A better understanding of the mechanisms that lead to tree mortality after fires and droughts is needed to design future policies that emphasise nature-based solutions including restoration and natural regeneration. 

This proposal presents a multi-level approach that aims at deciphering the mechanisms that underly vulnerability to fire and time-lagged post-fire mortality across the tropical forests in Amazon and Amazon-Cerrado transition. To achieve this aim, we will quantify fire vulnerability at three different scales and link them through an upscaling approach. First, we will identify the ecological mechanisms, reflected through functional traits, that explain why individuals and species die after fires occur. For this, we will focus on poorly understood traits that can be related to fire and/or hydraulic functioning. Second, at the community scale, we will examine how vegetation structure, community traits and microclimate affect the probability to burn, through an intensive characterisation of different vegetation types with multispectral and light detection and ranging (LIDAR) imagery. Third, we will use our our unique ground-dataset on functional traits, vegetation structure and moisture dynamics, and the latest state-of-art remotely sensed information on structure and water stress to predict the vulnerability of the Amazon forests and Amazon-Cerrado transitional forests. This information will be directly applicable for the detection of sensitive hotspots (areas particularly vulnerable to fire) through satellite products. We will deliver quantifiable early-warning metrics of ecosystem vulnerability to fire that can be mapped and incorporated into fire management policies.