Drylands store modest amounts of carbon in their soils and vegetation per unit area; however, as they cover 40% of the global land mass they store globally significant amounts of carbon overall. Rainfall is very variable between years, and these ecosystems respond rapidly to water availability. Therefore, carbon storage also varies greatly through time. Recent work has shown that dryland ecosystems may control variations in the global carbon cycle; however, there are significant uncertainties associated with understanding of carbon dynamics in dryland soils and vegetation, especially as climates change. Some researchers argue that drylands do not have the capacity to store or lose enough carbon to make any difference to the global amounts of carbon that are taken up by plants or soils, or lost to the atmosphere. Others argue that drylands may provide the 'missing-sink' in the global carbon cycle - helping to explain the stores of carbon needed to balance the global carbon budget, and that especially during wetter years, they have the capacity to store much more carbon than has previously been estimated. The current lack of consensus on the role of drylands in the global carbon cycle is hindering scientific ability to constrain the global carbon budget and understand future trends in the ability of the terrestrial carbon sink to mitigate climate change. This project will address this disagreement, providing robust analysis of existing data across a gradient of aridity and a range of plant types in a dryland region that contains the highest density of existing monitoring sites in the world. We will also undertake highly novel fieldwork, collecting new data which allow us to understand uncertainty in existing datasets that describe carbon storage and loss. We will then use new observations to evaluate which remotely sensed products, from existing satellite networks, are the most accurate at representing differences in carbon stocks in drylands. This element of the project is fundamental to understanding our planet, as it will enable more accurate global predictions of the carbon cycle and how this affects the global climate. Global modelling work argues that marginal, dryland ecosystems may control the global variability of carbon storage and loss and may also exert a profound control on the long-term trends of carbon storage and loss between the Earth and the atmosphere. This proposal will improve the empirical foundations of such model predictions. We contend that the predictions are likely to be true, but as yet have not been validated, nor understood well, in terms of the mechanisms that might underpin these controls. We will model the dynamics of vegetation in drylands, to test which vegetation models make the best predictions of growth and dieback, through wet and dry periods observed in the data that we collect in the first part of the project. Once we have established which vegetation models perform best, we will populate these models with appropriate parameters to predict how vegetation might respond to future climates, thus ensuring that the next step - to improve global model predictions of carbon loss and storage is made via a dialogue between empirical data collection and modelling. The project will deliver a fundamental improvement in our understanding of the carbon cycle in drylands, demonstrating empirically whether or not these landscapes have the capacity to control inter-annual variability and long-term trends in the land carbon sink. It will allow us to develop field techniques that can be exported to other landscapes, to constrain the uncertainty associated with measurements of ecosystem change. It will further allow us to understand and then recommend which globally available remote sensing products are best at characterising change in above-ground carbon stores in drylands. Finally it will permit us to make significant, data-based improvements to predictions of the global carbon cycle.

* Context The Earth's vegetation is changing in response to climate change, increased concentrations of CO2 in the atmosphere, and harvesting for fuel, food and building materials. These changes can accelerate or reduce climate change by altering the carbon cycle, and also affect the livelihoods of those who use natural resources in their day-to-day lives. One of the most important ways to understand vegetation change and its impacts, is to make careful measurements of the same patches of vegetation ("plots") repeatedly. Networks of these plots have produced surprising findings, challenging theory and models of vegetation responses to climate change. E.g. in Latin America, a network of these plots has shown that tropical forests are not soaking up as much carbon as predicted. Networks of these on-the-ground plot measurements are the only way to get a detailed view of how vegetation is currently changing. However at the moment, different researchers do not combine their data to understand regional patterns of change. This project will address this by bringing together researchers collecting plot data in southern African woodlands to share data and answer the big questions about what is happening to the vegetation in the region. The southern African woodlands are the largest savanna in the world (3 million km2), and support the livelihoods of 160M people. Many of these people are poor and depend upon the woodlands for 25% of their income and to support their agriculture. Theory and models suggest that these woodlands will be sensitive to increased atmospheric CO2 and other environmental changes underway: this is because, unlike forests, woodlands maintain a balance in the competition between trees and grasses, allowing both types of plant to co-exist. Small changes that benefit trees (such as more CO2 in the atmosphere) might rapidly change woodlands into a tree-dominated system. This would mean that they store more carbon, but might reduce the diversity of plants on the ground. It is also possible that human use of these woodlands, particularly wood harvesting for fuel, is altering their diversity and reducing the "services" that they provide. Currently we have no way to know if these changes are happening - satellite data and models can help, but need to be validated with plot measurements. * Aims and objectives Understanding the response of southern African woodlands to global change is the long-term goal of SEOSAW. It will do this by creating a regularly re-measured, systematic plot network. The stepping stones to this network are to: 1) develop an online data-sharing platform to exchange existing plot data so that we can look for signs of widespread change 2) combine NERC-funded data from 486 plots with data from 1,783 plots measured by others, to create a network that covers the whole region 3) use this new data set to better understand the processes that allow trees and grasses to co-exist, to allow modellers to make better predictions of future change 4) encourage researchers to make measurements in similar ways in the future, so that we can more easily detect changes 5) create a plan for future plot measurements that covers the whole region, and makes best use of the available time and money. * Who will benefit? SEOSAW will fill a large gap in the network of plots in tropical regions and benefit: - modellers of the Earth's vegetation will be able to test their models against reality in one of the most difficult to model biomes - scientists using satellite data to map vegetation will now be able to calibrate and validate their maps in all types of tropical vegetation - Those modelling the carbon cycle, who need to know how much carbon is being taken up by the woodlands Conservationists will also benefit, as SEOSAW will identify parts of the region that have unique or particularly diverse woodlands, helping to prioritise conservation efforts.