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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). 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., Funding Agencies|Swedish Research Council [2018-06139]; European Research Council (ERC) under the European Union [682472]

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., Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-06139]

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., Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-06139]

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. 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., Funding Agencies|Swedish Research Council [2018-06139]

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. 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., Funding Agencies|Swedish Research Council [2018-06139]

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. 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., Funding Agencies|Swedish Research Council [2018-06139]

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., Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-06139]

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. 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., Funding Agencies|Swedish Research Council [2018-06139]

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. 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., Funding Agencies|Swedish Research Council [2018-06139]

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., Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-06139]

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., Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-06139]