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

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.

The developmental impact of this project will be to contribute to poverty alleviation for the approximately 400 million people living in poverty in sub-Saharan Africa by improving the tools available to policy makers to manage ecosystem services (ES) to improve poor livelihoods in this region. More specifically, 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.
The main pathway to impact of WISER will be through improving the reliability of the tools and models that are used to manage the ES used by the rural poor in sub-Saharan Africa. Such an improvement in modelling will have direct impact on poverty alleviation by giving regional, national and international policy makers (the primary beneficiaries) the confidence to translate the multiple potential management actions that can be compared with such tools into evidence-based concrete policy actions which will allow alleviation of poverty through sustainable use of natural resources. In particular, WISER's tests of models that represent both the access to and utilisation of ES have the potential to have major impacts on poverty alleviation, given that this sort of disaggregation of beneficiaries of ES has been mostly overlooked in ES models, yet is clearly vital in devising pro-poor policies for ES management. The project is structured to ensure that the modelling needs of different types of policy makers are incorporated into the specific tests that we will conduct within the course of the project.
We will engage with both governmental and non-governmental organisations throughout the project via stakeholder interviews and workshops. WISER will actively listen to stakeholders throughout the project so they can identify their perceived needs for ES models: e.g. what outputs do they require and what spatial and temporal scale is needed? We will then use the results of these stakeholder engagements to inform our model comparison methods, ensuring we evaluate the ability of the ES models to fulfil the requirements of such decision makers. We will maintain regular communication with the stakeholders to help to build trust and provide the stakeholders with a sense of ownership and understanding of our results.
Our project team has a strong, grounded understanding of policy decision making at all levels. Our project partners include national staff members from Government Agencies, NGOs and civil society. Through collaboration with our project partners WISER will have access to local, regional and global dissemination networks that will help ensure that WISER deliver long lasting, poverty alleviating benefits, far beyond the lifetime and geographic span of the project.
The secondary beneficiaries include academics and researchers working in cognate fields. Results will be disseminated to academics via conferences, submission to professional newsletters and publication in international peer-reviewed journals. We aim publish in open access journals communicating research to the widest possible audience. Similarly, our data will be publicly available via an online repository