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

Lead Research Organisation: University of Southampton
Department Name: School of Biological Sciences

Abstract

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.

Planned Impact

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.

Publications

10 25 50
 
Description The aim of this project was to test if current modelling approaches for mapping key natural resources used by people (ecosystem services - ES) in sub-Saharan Africa were sufficiently good to inform policy. Our first output was a survey of current users (stakeholders) of existing ES models to see if they thought that the information they needed was being adequately provided, and if not, why. The main finding was that only 27% of the people we surveyed reported that adequate data existed for their policy needs. The second output - the main output of the project - was to use existing modelling approaches, as well new models based on simple modifications of existing models to better take users of natural resources into account, and test how well these predicted water availability, carbon stocks, firewood and charcoal use, and grazing resources across sub-Saharan Africa. We were particularly interested in seeing if more 'complex' models provided better predictive power with the goal of identifying minimum adequate models. This analysis - which used over 1600 datapoints from 16 separate datasets showed that existing modelling approaches were quite good at mapping the biophysical supply of natural resources, but much less good at identifying use of natural resources. We also found some evidence that complex models were often better and never worse than less complex models in predicting distributions of ES. Our findings are important as they show both that in some cases existing models do seem to provide useful information for policy makers - important in a data-poor region - but also that more effort is required to build better models linking demand for natural resources to their distributions.
Exploitation Route Our findings are important as they show both that in some cases existing models do seem to provide useful information for policy makers - important in a data-poor region - but also that more effort is required to build better models linking demand for natural resources to their distributions. So far, the overall PI on the project (Prof James Bullock) has taken some of our work and turned into a policy brief (http://www.wri.org/publication/guide-selecting-ecosystem-service-models-decision-making-lessons-sub-saharan-africa) in collaboration with the large international NGO World Resource Institute. We anticipate the findings will be of widespread interest in both the academic and modelling community once published (the main paper is currently in review)
Sectors Environment

 
Description The PI on the project has turned some of our key outputs into a policy brief in collaboration with the major international environmental NGO World Resources Institute: http://www.wri.org/publication/guide-selecting-ecosystem-service-models-decision-making-lessons-sub-saharan-africa The same resource is also available on the ESPA website: https://www.espa.ac.uk/publications/guide-selecting-ecosystem-service-models-decision-making-lessons-sub-saharan-africa
Sector Environment
Impact Types Societal

 
Description ERC Starting Grant
Amount € 1,485,000 (EUR)
Funding ID 680176 
Organisation European Research Council (ERC) 
Sector Public
Country Belgium
Start 07/2016 
End 07/2021
 
Description EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models
Amount £47,862 (GBP)
Funding ID NE/T00391X/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 09/2019 
End 08/2020
 
Description MobilES - Using mobile-phone technology to capture ecosystem service information
Amount £218,797 (GBP)
Funding ID ES/R009279/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 09/2018 
End 07/2020
 
Description Rurality as a vehicle for Urban Sanitation Transformation (RUST)
Amount £431,400 (GBP)
Funding ID ES/R006865/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 05/2018 
End 05/2020