Fuzzy Models to Inform Social and Environmental Indicator Selection for Conservation Impact Monitoring

Abstract

Conservation projects increasingly aim to deliver both environmental and social benefits. To monitor the success of these projects, it is important to pick indicators for which there is a reasonable expectation of change as a result of the project, and which resonate with project stakeholders. Results chains are widely used in conservation to describe the hypothesized pathways of causal linkages between conservation interventions and desired outcomes. We illustrate how, with limited additional information, results chains can be turned into fuzzy models of social-ecological systems, and how these models can be used to explore the predicted social and environmental impacts of conservation actions. These predictions can then be compared with the interests of stakeholders in order to identify good indicators of project success. We illustrate this approach by using it to select indicators for a water fund, an increasingly popular and multiobjective conservation strategy.

Mental models for conservation research and practice

“…At a more detailed level, stakeholder‐based models of a system can be used as the basis for mapping system dynamics (El Sawah, Guillaume, Filatova, Rook, & Jakeman, ; Game et al., ). When mental models capture interpretations of system dynamics, they can be used in system modeling to test the likely effects of different policy or management options (e.g., Gray, Chan, Clark, & Jordan, ; Özesmi & Özesmi, ).…”

“…Once a relationship has been defined, its nature can be recorded, permitting the researcher to integrate data on perceptions about the importance or strength of interactions influencing a system (e.g., Friedel, Grice, Marshall, & van Klinken, ) (Figure ). This additional information can be particularly important in determining which variables are useful indicators of program success, thereby contributing to one of the “principal, and often most challenging, tasks of applied conservation science”: monitoring (Game et al., , p. 1).…”

A novel approach to assessing the ecosystem‐wide impacts of reintroductions

“…, Game et al. ). The outcomes of this work provide a platform to assess the ecosystem risks from reintroductions, however defining explicit objectives for individual projects were not considered here.…”

“…Such analysis will likely rely on experts to elicit information on ecosystem structure among other components. In these situations, it is wise to use expert elicitation techniques to gain insight into the system (Martin et al 2012;Gregory et al 2012) and indeed there are a range of methods in the fuzzy logic literature for developing interaction networks (Zhang et al 2013, Game et al 2018. The outcomes of this work provide a platform to assess the ecosystem risks from reintroductions, however defining explicit objectives for individual projects were not considered here.…”

Translating Ecological Integrity terms into operational language to inform societies

“…This would also include the extrapolation to our system of interest of causal relationships between drivers (actually observed in our system) and pressures (observed in other systems). At this stage it would also be appropriate for the experts to consider how to relate each variable with a state that reflects a certain degree of increasing/decreasing EI and if necessary to assign the weights to each variable (e.g., adopting a fuzzy modelling approach; Game et al, 2017). Participatory approaches are useful to identify, and potentially score, the relevant variables that are related with the system's change (Game et al, 2017).…”