WISER: Which Ecosystem Service Models Best Capture the Needs of the Rural Poor?

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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.