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Visualization of causation in social-ecological systems

Thomas Banitz, Department of Ecological Modelling, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
Tilman Hertz, Stockholm Resilience Centre, Stockholm University, Sweden
Lars-Göran Johansson, Department of Philosophy, University of Uppsala, Sweden; Stockholm Resilience Centre, Stockholm University, Sweden
Emilie Lindkvist, Stockholm Resilience Centre, Stockholm University, Sweden
Rodrigo Martí­nez-Peña, Institute for Analytical Sociology, Linköping University, Sweden; Stockholm Resilience Centre, Stockholm University, Sweden
Sonja Radosavljevic, Stockholm Resilience Centre, Stockholm University, Sweden
Maja Schlüter, Stockholm Resilience Centre, Stockholm University, Sweden
Karl Wennberg, Department of Management, Stockholm School of Economics, Sweden; Institute for Analytical Sociology, Linköping University, Sweden
Petri Ylikoski, Sociology, University of Helsinki, Finland; Institute for Analytical Sociology, Linköping University, Sweden
Volker Grimm, Department of Ecological Modelling, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany

DOI: http://dx.doi.org/10.5751/ES-13030-270131

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Abstract

In social-ecological systems (SES), where social and ecological processes are intertwined, phenomena are usually complex and involve multiple interdependent causes. Figuring out causal relationships is thus challenging but needed to better understand and then affect or manage such systems. One important and widely used tool to identify and communicate causal relationships is visualization. Here, we present several common visualization types: diagrams of objects and arrows, X-Y plots, and X-Y-Z plots, and discuss them in view of the particular challenges of visualizing causation in complex systems such as SES. We use a simple demonstration model to create and compare exemplary visualizations and add more elaborate examples from the literature. This highlights implicit strengths and limitations of widely used visualization types and facilitates adequate choices when visualizing causation in SES. Thereupon, we recommend further suitable ways to account for complex causation, such as figures with multiple panels, or merging different visualization types in one figure. This provides caveats against oversimplifications. Yet, any single figure can rarely capture all relevant causal relationships in an SES. We therefore need to focus on specific questions, phenomena, or subsystems, and often also on specific causes and effects that shall be visualized. Our recommendations allow for selecting and combining visualizations such that they complement each other, support comprehensive understanding, and do justice to the existing complexity in SES. This lets visualizations realize their potential and play an important role in identifying and communicating causation.

Key words

causal relationship; complex systems; illustration; visualization

Copyright © 2022 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.

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Ecology and Society. ISSN: 1708-3087