Visualization of causation in social-ecological systems

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 impor...

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Bibliographic Details
Published in:Ecology and society 2022, Vol.27 (1), p.31, Article art31
Main Authors: Banitz, Thomas, Hertz, Tilman, Johansson, Lars-Göran, Lindkvist, Emilie, Martí­nez-Peña, Rodrigo, Radosavljevic, Sonja, Schlüter, Maja, Wennberg, Karl, Ylikoski, Petri, Grimm, Volker
Format: Article
Language:English
Subjects:
causal relationship
Causality
Causation
Communication
Complex systems
Complexity
illustration
Social-ecological systems
Subsystems
Systems
Variables
Visualization
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Summary: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.
ISSN:1708-3087
1708-3087
DOI:10.5751/ES-13030-270131