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Mangrove forests are unique intertidal ecosystems connecting the land- and seascape. They provide habitat to terrestrial and marine species, sustain the livelihoods of millions of mostly poor people globally, and are considered as high priority habitats in climate change mitigation strategies, due to their extraordinary carbon sink capacity. Mangroves forests are degraded globally, with land use change being the single most serious threat at present. Successful restoration/rehabilitation of diverse, functional, resource-rich and resilient mangrove forests is a major development challenge in many countries, including Indonesia. The so called Blue Revolution - the conversion of mangroves to (unsustainable) aquaculture ponds in the 80s and 90s - is one major reason why the country has lost 40% of its mangroves over the last three decades. This has caused manifold problems for people's lives. Halting and reversing Indonesia's loss of mangrove natural assets is key to improve coastal livelihoods and reduce poverty. The Indonesian government currently spends around $13 million a year for planting mangroves on degraded areas. Most planting projects in Indonesia and elsewhere in the world have failed, and it is mostly understood why. There are however numerous critical information gaps in understanding how successful the "successful" projects are in regards to recreating diverse and functional self-organising and self-maintaining systems. CoReNat will investigate outcomes of established community-based mangrove restoration/ rehabilitation (R/R) projects in the heart of Wallacea - North-Sulawesi - Indonesia, to unravel whether these mangroves are "As good as (G)Old?". The overall project aims are to assess whether mangrove ecosystem biodiversity, functions, resilience and service provision have been restored, and to make evidence-based recommendations for maximizing the success of future R/R efforts in Wallacea (and beyond). Combining UK and Indonesian experience, expertise and scientific excellence, CoReNat will provide evidence-based recommendations to relevant stakeholder to guide future ecological R/R efforts. CoReNat takes a novel interdisciplinary approach to deliver a comprehensive ecosystem evaluation of established restored/rehabilitated and adjacent natural (reference) mangroves, bringing together paleoecology, geoscience, botany, zoology, environmental microbiology, ecological network analysis combined with next generation sequencing, toxicology and bioexploration. CoReNat will - provide new data on the region's (mangrove-associated) biodiversity and species interactions, for conserved as well as for rehabilitated/restored mangrove forests - apply and generate innovative new tools for the field of mangrove restoration - provide data that will allow a better understanding of the biodiversity, functioning and services of mono-specific versus multi-specific replanted mangroves - support the provision of solutions to mangrove conservation, restoration/ rehabilitation and management - explore current local use of conserved and restored mangroves, as well as potential new avenues for business and innovation, to help balance Indonesia's need for conservation with economic development

It is widely acknowledged that poor rural communities are frequently highly dependent on ecosystem services (ES) for their livelihoods, especially as a safety net in times of hardship or crisis. However, a major challenge to the understanding and management of these benefit flows to the poor is a lack of data on the supply, demand and use of ecosystem services by the poor, particularly in the developing world where dependence on ES is often highest. Recent work suggests that errors associated with the commonly used global proxies (eg. benefits transfer) are likely to be substantial and therefore confuse or worse, misdirect, policy formulation or management interventions (e.g. perverse subsidies). Given these issues, recent improvements in integrated modelling platforms - in some cases founded on desktop process-based models - which aim to provide improved and dynamic maps of current and future distributions of ES have much to offer ES-based poverty alleviation interventions and policy. While these next generation process-based models appear to have a role to play in ES-based poverty alleviation efforts, the level of sophistication and data needs that is required to deliver policy relevant information is poorly understood. It is, for example, unclear whether even the most sophisticated process-based biophysical model is able to provide sufficiently accurate information for regional- or local-scale policy decision making when based on globally available datasets. Similarly, there has been no attempt to quantify the degree to which disaggregation of beneficiaries is necessary within integrated modelling platforms to provide information on managing natural assets that is relevant to the poorest people. Such analyses are vital to ensure that next generation models produce useful and credible results as efficiently as possible - that is, with a minimum investment in data collection and bespoke model development. We will evaluate the effectiveness of a range of current modelling approaches of varying degrees of complexity for mapping at least six ecosystem services - crop production, stored carbon, water availability, non-timber forest products (NTFPs), grazing resources, and pollination - at multiple spatial scales across sub-Saharan Africa. We will assess model performance based on two broad metrics: model data requirements and the usefulness to decision-making. Firstly, we will evaluate the data requirements of each modelling tier, using data availability, spatial resolution and uncertainty to score in the intensity of the required inputs. Those models with intensive data requirements will be scored poorly. Secondly, we will evaluate the usefulness of the model in a decision-making process using statistical binary discriminator tests. We will use the same approach to evaluate the impact of consideration of beneficiaries on decision making by comparing the biophysical model outputs with both socioeconomic measures and models also using binary discriminator tests. Our goal in this project is to ascertain the degree of complexity of modelling that needs to be applied to map ES at resolutions that are useful for poverty alleviation. The findings of this project will enable decision makers to: 1) best use existing ES models to inform national and regional land use/cover change policies supporting ES management and promoting equality and justice amongst the beneficiaries of these services; and 2) set priorities determining where scarce resources should be invested to improve effective management of ES. Thus, WISER may help improve the lives of the approximately 400 million people living in poverty in sub-Saharan Africa by evaluating the tools available to policy makers in this region.

Mangrove forests are often associated with the smell of rotten eggs and swarms of mosquitos. This may be true but at the same time these forests are unique and extremely valuable. Mangrove trees grow in challenging environments surviving hot, muddy and salty conditions as they thrive at the margin of land and sea in the tropics and subtropics. Mangrove ecosystems provide essential habitats for many animal species, they help filtering pollutants and protect the coast against erosion. Moreover, mangroves play a crucial role in combating climate change as they capture and store large amounts of carbon from the atmosphere. In fact, these forests store carbon faster than most land ecosystems. The trees store carbon not only in their wood and leaves, but also in those smelly muddy soils. Despite all these benefits, mangroves are heavily threatened as sea level rise may cause forest drowning and people are increasingly modifying coastal landscapes and interfering with the natural processes on which mangroves depend. The impacts of such pressures on mangrove forests are still unclear, but the consequences may be drastic mangrove loss and reductions in carbon storage. Mangrove trees flourish under very specific conditions. They grow well under regular inundation by tides, but they cannot survive prolonged flooding. Hence mangroves will need to keep raising the bed on which they grow to cope with rising sea levels. Mangroves may accomplish by trapping sediments from the land and the sea with their roots. In addition, dead roots, leaves and branches accumulate within the muddy soils. This helps mangroves to gain elevation and the build-up of dead plant material creates carbon-rich sediments. Now, essentially two possibilities emerge. If mangroves keep up with sea level rise by accumulating carbon-rich plant material in their soils, then carbon stocks can actually increase. However, if sea level rise outpaces mangrove soil buildup, then tree mortality will reduce carbon storage. Limits to the adaptability of mangrove forests to sea level rise exist and these limits are influenced by human activities. Building of river dams, for example, reduces the delivery of sediment to the coast, while this sediment is needed to help raising mangroves and enable continued carbon storage. Clearly, mangrove environments are highly complex and in order to protect these valuable environments, improved understanding and abilities to predict their future are urgently needed. In this project, we will unravel the processes that control how and how much carbon is stored in mangrove forests and develop new computer models to investigate the impacts of sea level rise and human activities on future carbon accumulation. We have selected three sites in Colombia (South America) where mangrove trees reach up to 40 meters (!) making these forests true carbon storage hotspots. First, we will obtain soil samples up to a depth of 2 meters. We will estimate their carbon content, how fast that carbon has accumulated during the past, and where the carbon is coming from. We will also use microscopic plant remains preserved in the soil to discover what mangrove species have grown there in the past and whether this has influenced carbon accumulation. Third, we will develop a model capable of simulating how entire deltas and estuaries with mangrove vegetation evolve over tens to hundreds of years. Finally, we will use this new model to investigate the fate of mangrove forests under rising sea levels and varying sediment supply, and impacts on future carbon accumulation. Colombian high-school students and teachers from will participate in fieldwork and will present their work in science fairs for the general public to increase the awareness of the values of mangrove forests. We will also work together with our project partners to use our findings to support the development of sustainable management strategies in order to safeguard mangrove environments.

* 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.