Iterative scenarios for social-ecological systems

Abstract

Managing social-ecological systems toward desirable regimes requires learning about the system being managed while preparing for many possible futures. Adaptive management (AM) and scenario planning (SP) are two systems management approaches that separately use learning to reduce uncertainties and employ planning to manage irreducible uncertainties, respectively. However, each of these approaches have limitations that confound management of social-ecological systems. Here, we introduce iterative scenarios (IS), a systems management approach that is a hybrid of the scopes and relationships to uncertainty and controllability of AM and SP that combines the "iterativeness" of AM and futures planning of SP. Iterative scenarios is appropriate for situations with high uncertainty about whether a management action will lead to intended outcomes, the desired benefits are numerous and cross-scale, and it is difficult to account for the social implications around the natural resource management options. The value of iterative scenarios is demonstrated by applying the approach to green infrastructure futures for a neighborhood in the city of Cleveland, Ohio, U.S., that had experienced long-term, systemic disinvestment. The Cleveland green infrastructure project was particularly well suited to the IS approach given that learning about environmental factors was necessary and achievable, but what would be socially desirable and possible was unknown. However, iterative scenarios is appropriate for many social-ecological systems where uncertainty is high as IS accommodates real-world complexity faced by management.

Keywords: adaptive management; futures; green infrastructure; iterative scenarios; scenario planning; social-ecological systems; structured learning.

Fig. 1.

Graphic representation of the process for carrying out iterative scenarios. The initial phase is scenario construction that develops future narratives based around key uncertainties around what will be desirable and plausible in the future for the system being managed and the greater motivating social-ecological context. The first iteration (1°) involves consideration of major contrasts about how the future will unfold and what will be desirable and plausible. A critical next step is translating these future narratives into testable adaptive management (AM) schemes (represented by the hourglass symbol) that seek to learn about natural resource functioning as it will be necessary to inform multiple future management needs. Several schemes are implemented and tested simultaneously (ideally) in a horse-race style that will inform management for different social-ecological futures. Assessment phase is next, which involves the selection of the most desirable scheme based on learning about their performance and consideration of evolving motivating social-ecological context. The most desirable scheme emerges as a broad vision that will likely require further learning about uncertainties in social-ecological futures. Thus, the process is iterated starting with refined future narratives constructed around the most desirable scheme from the previous iteration and again considering further evolution in the motivating context. Alternatively, shifts in the motivating social-ecological context, e.g., a high-level policy change, could result in refining and further testing a previously less desirable scheme. The number of schemes to test at the implementation phase across iterations is dependent on the goals and capacities of the actors and their system’s complexity. The scenario construction-implementation-assessment process is iterated indefinitely, adjusting to the evolving contexts and continual refinement of uncertainties to be resolved. Ellipses graphically represent the ongoing management decisions and actions and the subsequent iteration of the process.

Fig. 2.

Four scenarios—Business-as-usual, City meadows, Low-budget rain gardens, and High-budget rain gardens—were schemed for a vacant land management program intended to identify desirable green infrastructure strategies in Cleveland.