Browsing by Author "Balbi, Stefano"

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    A continental-scale validation of ecosystem service models
    (Springer, 2019-04-22) Willcock, Simon; Hooftman, Danny A. P.; Balbi, Stefano; Blanchard, Ryan; Dawson, Terence P.; O’Farrell, Patrick J.; Hickler, Thomas; Hudson, Malcolm D.; Lindeskog, Mats; Martinez-Lopez, Javier; Mulligan, Mark; Reyers, Belinda; Shackleton, Charlie; Sitas, Nadia; Villa, Ferdinando; Watts, Sophie M.; Eigenbrod, Felix; Bullock, James M.
    Faced with environmental degradation, governments worldwide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by the existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the demand side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being.
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    Human dependence on natural resources in rapidly urbanising South African regions
    (IOP Publishing, 2019-03-29) Balbi, Stefano; Selomane, Odirilwe; Sitas, Nadia; Blanchard, Ryan; Kotzee, Ilse; O’Farrell, Patrick; Villa, Ferdinando
    Enhancing the governance of social-ecological systems for more equitable and sustainable development is hindered by inadequate knowledge about how different social groups and communities rely on natural resources. We used openly accessible national survey data to develop a metric of overall dependence on natural resources. These data contain information about households' sources of water, energy, building materials and food. We used these data in combination with Bayesian learning to model observed patterns of dependence using demographic variables that included: gender of household head, household size, income, house ownership, formality status of settlement, population density, and in-migration rate to the area. We show that a small number of factors—in particular population density and informality of settlements—can explain a significant amount of the observed variation with regards to the use of natural resources. Subsequently, we test the validity of these predictions using alternative, open access data in the eThekwini and Cape Town metropolitan areas of South Africa. We discuss the advantages of using a selection of predictors which could be supplied through remotely sensed and open access data, in terms of opportunities and challenges to produce meaningful results in data-poor areas. With data availability being a common limiting factor in modelling and monitoring exercises, access to inexpensive, up-to-date and free to use data can significantly improve how we monitor progress towards sustainability targets. A small selection of openly accessible demographic variables can predict household's dependence on local natural resources.