Application of the SES Framework for Model-based Analysis of the Dynamics of Social-Ecological Systems

Author(s): Schlüter, M., J. Hinkel, P. W. G. Bots, and R. Arlinghaus
In: Ecology and Society 19(1): 36
Year: 2014
Type: Journal / article
Link to centre authors: Schlüter, Maja
Full reference: Schlüter, M., J. Hinkel, P. W. G. Bots, and R. Arlinghaus. 2014. Application of the SES framework for model-based analysis of the dynamics of social-ecological systems. Ecology and Society 19(1): 36. http://dx.doi.org/10.5751/ES-05782-190136

Summary

Social-ecological systems (SES) are dynamic systems that continuously
change in response to internal or external pressures. A better
understanding of the interactions of the social and ecological systems
that drive those dynamics is crucial for the development of sustainable
management strategies. Dynamic models can serve as tools to explore
social-ecological interactions; however, the complexity of the studied
systems and the need to integrate knowledge, theories, and approaches
from different disciplines pose considerable challenges for their
development.

We assess the potential of Ostrom’s general SES framework SESF) to guide a systematic and transparent process of model development in light of these difficulties. We develop a stepwise procedure for applying SESF to identify variables and their relationships relevant for an analysis of the SES. In doing so we demonstrate how the hierarchy of concepts in SESF and the identificatio of social-ecological processes using the newly introduced process relationships can help to unpack the system in a systematic and
transparent way. We test the procedure by applying it to develop a
dynamic model of decision making in the management of recreational
fisheries.

The added value of the common framework lies in the guidance
it provides for (1) a structured approach to identifying major variables
and the level of detail needed, and (2) a procedure that enhances model
transparency by making explicit underlying assumptions and choices made
when selecting variables and their interactions as well as the theories
or empirical evidence on which they are based. Both aspects are of
great relevance when dealing with the complexity of SES and integrating
conceptual backgrounds from different disciplines.

We discuss the advantages and difficulties of the application of SESF for model development, and contribute to its further refinement.

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