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Dynamical systems modeling for structural understanding of social-ecological systems: A primer

A new paper is just out from the CauSES team about dynamical systems modeling!

Dynamical systems modeling (DSM) explores how a system evolves in time when its elements and the relationships between them are known. The basic idea is that the structure of a dynamical system, expressed by coupled differential or difference equations, determines attractors of the system and, in turn, its behavior. This leads to structural understanding that can provide insights into qualitative properties of real systems, including ecological and social-ecological systems (SES).

Fig. 1
Fig. 1 from the publication: (A) Conceptualization of a DSM corresponding to a SES based on case study of collapse of a Baltic cod fishery. (B) Mathematical formalization of the SES using a system of ordinary differential equations (ca, cj, s, denote adult cod, juvenile cod and sprat abundance respectively). (C) Phase portrait of the DSM. The red dot is the initial state.

DSM generally does not aim to make specific quantitative predictions or explain singular events, but to investigate consequences of different assumptions about a system’s structure. SES dynamics and possible causal relationships in SES get revealed through manipulation of individual interactions and observation of their consequences. Structural understanding is therefore particularly valuable for assessing and anticipating the consequences of interventions or shocks and managing transformation toward sustainability.

Taking into account social and ecological dynamics, recognizing that SES may operate on different time scales simultaneously and that achieving an attractor might not be possible or relevant, opens up possibilities for DSM setup and analysis. This also highlights the importance of assumptions and research questions for model results and calls for closer connection between modeling and empirics.

Understanding the potential and limitations of DSM in SES research is important because the well-developed and established framework of DSM provides a common language and helps break down barriers to shared understanding and dialog within multidisciplinary teams. In this primer we introduce the basic concepts, methods, and possible insights from DSM.

Our target audience are both beginners in DSM and modelers who use other model types, both in ecology and SES research.

Highlights from the paper Dynamical systems modeling for structural understanding of social-ecological systems: A primer:

  • Complex temporal dynamics of human-nature interactions is one of the greatest challenges for understanding and managing social-ecological systems (SES).
  • Dynamical systems modeling (DSM) could provide the necessary theoretical framework for future research and help shape our understanding and management of SES.
  • Shifting research focus from equilibrium thinking and asymptotic dynamics to out-of-equilibrium states and transient dynamics could offer alternative explanations for observed phenomena in SES.
  • Combining DSM with empirical research methods and agent based modeling can help overcome some limitations of DSM, such as relying on simplified assumptions.

Reference: Radosavljevic, S., Banitz, T., Grimm, V., Johansson, L.-G., Lindkvist, E., Schl├╝ter, M., & Ylikoski, P. (2023). Dynamical systems modeling for structural understanding of social-ecological systems: A primer. Ecological Complexity, 56, 101052. https://doi.org/10.1016/j.ecocom.2023.101052

Model-derived causal explanations are inherently constrained by hidden assumptions and context

Models are widely used for investigating cause-effect relationships in complex systems. Within the CauSES project we were interesting in how computational models are used to make causal claims. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling.

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Fig. 1 from the associated publication: Overview of entities taken into account in the 15 models analyzed. They are grouped into cod, sprat, herring and other species populations, the environment (represented by different factors), and fishers. Box sizes and grey numbers show the number of models in which the entity was included (out of 15 models in total). The additional boxes within larger ones show the separation of cod, sprat or herring populations into age groups, size groups or life stage groups, and the separation of fishers into multiple fleets.

As an example, we here compare ecological models dealing with the dynamics and collapse of cod in the Baltic Sea. The models addressed different specific questions, but also vary widely in system conceptualization and complexity. With each model, certain ecological factors and mechanisms were analyzed in detail, while others were included but remained unchanged, or were excluded.

Model-based causal analyses of the same system are thus inherently constrained by diverse implicit assumptions about possible determinants of causation. In developing recommendations for human action, awareness is needed of this strong context dependence of causal claims, which is often not entirely clear. Model comparisons can be supplemented by integrating findings from multiple models and confronting models with multiple observed patterns.

The paper “Model-derived causal explanations are inherently constrained by hidden assumptions and context: The example of Baltic cod dynamics” is published at Environmental Modeling and Software

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.

Figure 1. Multiple types of visualizations of causal relationships were assessed in the study. Examples include various versions of diagrams of objects and arrows, such as conceptual diagrams, formal causal diagrams and network diagrams, and of X-Y plots and X-Y-Z plots.

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.

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.

The article “Visualization of causation in social-ecological systems” is published in the open access journal Ecology & Society.