WP1. Designing a framework for analysis

A Blueprint Protocol (Seppelt et al. 2012) will serve as the backbone analytical tool to underpin data collection, testing the system scale hypotheses and scenario construction. Given the need for: (1) a broad, multi-dimensional understanding of poverty (Hulme et al. 2001) and its converse, wellbeing; (2) a necessary focus on only a subset of ES of importance for poverty alleviation; and (3) the engagement of a wide range of stakeholders, close collaboration with partners in the development of the protocol is fundamental. Collaboration in the design, production and delivery of research is critical to secure stakeholder engagement and to produce credible outputs (Cash et al. 2003). After an initial preliminary selection of suitable stakeholders (see Box 1), further stakeholder mapping will help minimise the risk of marginalising underrepresented actors (Prell et al. 2009).

To formalise the conceptual understandings of our stakeholders, we will use Bayesian Belief Network techniques (BBNs; see Box 2). BBNs are an ideal tool for modelling the causes and effects of different drivers of change, management impacts and other factors and are also adept at representing uncertainty in social-ecological systems. We will use them both to (1) formalise our Blueprint Protocol (WP1) by representing current land use/ES/wellbeing linkages at local, regional and national scale (Smith et al. 2011), and (2) to allow the stakeholders to construct quantitative future scenarios (WP5). BBNs have been used widely by our team for exploring the impacts of land cover change under different scenarios (e.g. in the UK NEA, Haines-Young et al. 2011) and by others in the context of community-led decision-making in African woodlands (Bacon et al. 2002; Lynam et al. 2004). They provide a graphical method of displaying the relationships between variables – crucial to aid stakeholder participation. Variable linkages are represented by conditional probability tables, which are parameterised by elicitation involving experts and local stakeholders or from data. The strength of BBNs lie in the transparent way of dealing with complexity, as well as their capacity to deal with a combination of expert opinion, empirical data and missing data. To ensure close integration between WP1&5, the same staff will undertake the stakeholder engagement for both WP1&5 and activities will be conducted with the same groups, sometimes at the same meetings.