Amazon-SOS: a Safe Operating Space for Amazonian Forests

Lead Research Organisation: University of Exeter
Department Name: Geography


The overall aim of this project is to determine and communicate the risk of significant change to the Amazon rainforest caused by anthropogenic disturbance and climate change. We will address a fundamental issue of our time, on the likelihood of Amazon rainforest dieback in the 21st century and identify regions that are most susceptible. We will combine this new knowledge with policies and scenarios developed by key stakeholders to co-design a Safe-Operating-Space for Amazonia.
To address the iconic issue of Amazon dieback we will advance new ecological understanding of how forests grow, decline and recover following disturbance from climate extremes, forest fire and deforestation and their interaction in the context of 21st Century global warming. We will build novel datasets using a new forest plot network, drones and satellites to produce near-real-time maps of the risk to forests from climate, and track individual large-tree mortality across the basin. Together this information will be used in mathematical models to help estimate the risk of future forest dieback. We will join this work with models used to predict the effects of land use (forest conversion, degradation) on forest function, and the ecosystem services these forests provide to humanity. The outputs will enable us to deliver new information to policy makers regarding future options for land use, helping them to build optimal land use pathways that minimise the risks that may arise out of large-scale forest loss or dysfunction in Amazonia.
The Amazon forest plays a vital role in the world's climate. In addition, by annually absorbing 5-10% of human-related CO2 emissions via vegetation growth, the region acts as a large brake on climate change. Climate extremes (eg drought), forest fires and deforestation reverse this process, causing net emissions to the atmosphere. If this were to happen on a large enough scale, via increased forest loss or increased rates of climate change - or their interaction - the resulting positive effect on global CO2 and climate change, would make the already-challenging Paris climate targets virtually impossible.
In short, climate change, forest fires and deforestation have been identified as major intensifying and interacting threats to Amazonia. A substantive loss of Amazonian forest, also known as "Amazon dieback", would have huge negative consequences for human well-being, biodiversity, biogeochemical cycling, and regional and global climate. However, the level of global climate change combined with human disturbance that could trigger large-scale dieback is not known. Climate change is predicted to become more intense in the region alongside increases in human-driven deforestation and forest degradation (e.g fires, logging). Their impacts are poorly understood because of a lack of data, and because models cannot currently represent the key processes well enough.
We have gathered leading UK and S American scientists in the fields of ecology, ecophysiology, Earth observation (using satellites) and the mathematical modelling of vegetation growth, land-use and climate as applied to Amazonia. We are uniquely positioned to make a step-change in understanding the combined effects of climate stress and human disturbance on Amazonia. Our measurements will build new knowledge about intact and disturbed forests, their stability and the physiology driving their stress responses. These knowledge advances will enable new modelling of forest-climate-land-use interactions which we will use to inform policymakers. We will engage with stakeholders from state to international levels to co-develop land-use scenarios that minimise risk in future climate and forest ecosystem services. Overall, we propose multiple large and integrated advances in empirical and modelling studies of the forests of Amazonia, and will build a science-policy dialogue that delivers significant impact locally, regionally and globally.


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