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When reasoning about causes of sustainability problems and possible solutions, sustainability scientists rely on disciplinary-based understanding of cause–effect relations. These disciplinary assumptions enable and constrain how causal knowledge is generated, yet they are rarely made explicit. In a multidisciplinary field like sustainability science, lack of understanding differences in causal reasoning impedes our ability to address complex sustainability problems. To support navigating the diversity of causal reasoning, we articulate when and how during a research process researchers engage in causal reasoning and discuss four common ideas about causation that direct it. This articulation provides guidance for researchers to make their own assumptions and choices transparent and to interpret other researchers’ approaches. Understanding how causal claims are made and justified enables sustainability researchers to evaluate the diversity of causal claims, to build collaborations across disciplines, and to assess whether proposed solutions are suitable for a given problem.

Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness of hidden assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies.

This open access book is about causal thinking and the use of causal language, with a focus on introducing philosophical ideas about causation to students and researchers of Social-Ecological  Systems (SES). It takes a systematic approach to three central topics: the meanings of different causal expressions, sufficiency of evidence for inferences from observations to causal relations, and how to handle the complexity of causal relations in social-ecological systems. Consequently, the book is divided into three parts. In the first part the authors analyse and discuss the use of causal idiom in ordinary language, and in the second part they scrutinise the use of causal concepts and causal inference in science. Finally, the authors discuss causal reasoning about social-ecological systems in multi- and interdisciplinary contexts. This book provides an analysis of the concept of causation useful in the empirical sciences, where causal notions and idioms  often are used without sufficient reflection. Empirical sciences often use causal idiom drawn from ordinary language, and similarly there is little formalisation of causal language and technical concepts in the humanities and social sciences. This book is a valuable resource for the application of current philosophical discussions about the concept of causation, in particular when applied to the analysis of social-ecological systems, but also when applied to research in the sciences and humanities.

European grassland management has often favoured high production through frequent mowing and heavy fertilisation over biodiversity conservation, which is typically supported by less intensive management. Besides management, climate change and extremes are increasingly affecting grassland productivity and biodiversity, requiring timely adaptation of management practices. Here, we describe the development of a prototype Digital Twin (pDT) of grassland biodiversity dynamics intended to support researchers, farmers or regulatory decision-makers in monitoring the current state of selected grassland sites and projecting their future state under various management and climate scenarios.

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.

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The use of mechanistic population models as research and decision-support tools in ecology and ecological risk assessment is increasing. This growth has been facilitated by advances in technology, allowing the simulation of more complex systems, as well as by standardized approaches for model development, documentation, and evaluation. Mechanistic population models are particularly useful for simulating complex systems, but the required model complexity can make them challenging to communicate. Conceptual diagrams that summarize key model elements, as well as elements that were considered but not included, can facilitate communication and understanding of models and increase their acceptance as decision-support tools. Currently, however, there are no consistent standards for creating or presenting conceptual model diagrams, and both terminology and content vary widely. Here we argue that greater consistency in conceptual model diagram development and presentation is an important component of good modeling practice, and we provide recommendations, examples, and a free web app (pop-cmd.com) for achieving this for population models used for decision support in ecological risk assessments.

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.

Assessing and predicting the persistence of populations is essential for the conservation and control of species. Here, we argue that local mechanisms require a better conceptual synthesis to facilitate a more holistic consideration along with regional mechanisms known from metapopulation theory. We summarise the evidence for local buffer mechanisms along with their capacities and emphasise the need to include multiple buffer mechanisms in studies of population persistence. We propose an accessible framework for local buffer mechanisms that distinguishes between damping (reducing fluctuations in population size) and repelling (reducing population declines) mechanisms. We highlight opportunities for empirical and modelling studies to investigate the interactions and capacities of buffer mechanisms to facilitate better ecological understanding in times of ecological upheaval.

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]

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Individual-based modeling is widely applied to investigate the ecological mechanisms driving microbial community dynamics. In such models, the population or community dynamics emerge from the behavior and interplay of individual entities, which are simulated according to a predefined set of rules. If the rules that govern the behavior of individuals are based on generic and mechanistically sound principles, the models are referred to as next-generation individual-based models. These models perform particularly well in recapitulating actual ecological dynamics. However, implementation of such models is time-consuming and requires proficiency in programming or in using specific software, which likely hinders a broader application of this powerful method. Here we present McComedy, a modeling tool designed to facilitate the development of next-generation individual-based models of microbial consumer-resource systems. This tool allows flexibly combining pre-implemented building blocks that represent physical and biological processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems is demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. With this article, we provide a versatile tool for developing next-generation individual-based models that can foster understanding of microbial ecology in both research and education.

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.

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.

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.

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]

Biodiversity is a key driver of ecosystem functioning, while disturbances are a key driver of biodiversity. Consequently, disturbances crucially influence ecosystem functioning– both directly via affecting ecosystem processes but also indirectly via altering biodiversity. We thus need to disclose the joint relationships between disturbances, biodiversity and functioning (DBF) to understand and predict ecosystem dynamics under realistic conditions. However, biodiversity responses to disturbances have so far insufficiently been studied together with biodiversity effects on functions. For many ecosystems, such integrative exploration of DBF relationships would require too extensive manipulations and observations over unfeasible spatial and temporal scales. We argue that microbial systems offer a bright perspective to overcome these limitations, and present a roadmap for doing so. Microbial systems allow us exposing different, well characterized communities to multiple, reproducible disturbance regimes, and precisely measuring both biodiversity and associated functions over time. Comprehensive data can be obtained by systematically varying and replicating representative environmental scenarios. These data can further be explored and explained with computational models. Microbial systems thus reveal mechanisms that underlie DBF relationships and allow scrutinizing ecological hypotheses. This advancement of theory will be essential for ecology as a whole. It is particularly relevant in the context of global change, which is expected to promote disturbances as well as loss of biodiversity and functions in many ecosystems

Biodiversity is a key driver of ecosystem functioning, while disturbances are a key driver of biodiversity. Consequently, disturbances crucially influence ecosystem functioning—both directly via affecting ecosystem processes but also indirectly via altering biodiversity. We thus need to disclose the joint relationships between disturbances, biodiversity and functioning (DBF) to understand and predict ecosystem dynamics under realistic conditions. However, biodiversity responses to disturbances have so far insufficiently been studied together with biodiversity effects on functions. For many ecosystems, such integrative exploration of DBF relationships would require too extensive manipulations and observations over unfeasible spatial and temporal scales. We argue that microbial systems offer a bright perspective to overcome these limitations, and present a roadmap for doing so. Microbial systems allow us exposing different, well-characterized communities to multiple, reproducible disturbance regimes and precisely measuring both biodiversity and associated functions over time. Comprehensive data can be obtained by systematically varying and replicating representative environmental scenarios. These data can further be explored and explained with computational models. Microbial systems thus reveal mechanisms that underlie DBF relationships and allow scrutinizing ecological hypotheses. This advancement of theory will be essential for ecology as a whole. It is particularly relevant in the context of global change, which is expected to promote disturbances as well as loss of biodiversity and functions in many ecosystems.

Both climate change and land use regimes affect the viability of populations, but they are often studied separately. Moreover, population viability analyses (PVAs) often ignore the effects of large environmental gradients and use temporal resolutions that are too coarse to take into account that different stages of a population's life cycle may be affected differently by climate change. Here, we present the High-resolution Large Environmental Gradient (HiLEG) model and apply it in a PVA with daily resolution based on daily climate projections for Northwest Germany. We used the large marsh grasshopper (LMG) as the target species and investigated (1) the effects of climate change on the viability and spatial distribution of the species, (2) the influence of the timing of grassland mowing on the species and (3) the interaction between the effects of climate change and grassland mowing. The stage- and cohort-based model was run for the spatially differentiated environmental conditions temperature and soil moisture across the whole study region. We implemented three climate change scenarios and analyzed the population dynamics for four consecutive 20-year periods. Climate change alone would lead to an expansion of the regions suitable for the LMG, as warming accelerates development and due to reduced drought stress. However, in combination with land use, the timing of mowing was crucial, as this disturbance causes a high mortality rate in the aboveground life stages. Assuming the same date of mowing throughout the region, the impact on viability varied greatly between regions due to the different climate conditions. The regional negative effects of the mowing date can be divided into five phases: (1) In early spring, the populations were largely unaffected in all the regions; (2) between late spring and early summer, they were severely affected only in warm regions; (3) in summer, all the populations were severely affected so that they could hardly survive; (4) between l

Resilience of soils, i.e., their ability to maintain functions or recover after disturbance, is closely linked to the root-soil interface, the soil's power house. However, the limited observability of key processes at the root-soil interface has so far limited our understanding of how such resilience emerges. Here, we hypothesize that resilience emerges from self-organized spatiotemporal patterns which are the result of complex and dynamic feedbacks between physical, chemical, and biological processes occurring in the rhizosphere. We propose that the combination of modern experimental and modeling techniques, with a focus on imaging approaches, allows for understanding the complex feedbacks between plant resource acquisition, microbiome-related plant health, soil carbon sequestration, and soil structure development. A prerequisite for the identification of patterns, underlying processes, and feedback loops is that joint experimental platforms are defined and investigated in their true 2D and 3D geometry along time. This applies across different scientific disciplines from soil physics/chemistry/microbiology to plant genomics/physiology and across different scales from the nano/microscopic scale of the root soil interface, over the radial profiles around single roots, up to the root architecture and plant scale. Thus, we can move beyond isolated reductionist approaches which have dominated in rhizosphere research so far.

Biodiversity is a key driver of ecosystem functioning, while disturbances are a key driver of biodiversity. Consequently, disturbances crucially influence ecosystem functioning– both directly via affecting ecosystem processes but also indirectly via altering biodiversity. We thus need to disclose the joint relationships between disturbances, biodiversity and functioning (DBF) to understand and predict ecosystem dynamics under realistic conditions. However, biodiversity responses to disturbances have so far insufficiently been studied together with biodiversity effects on functions. For many ecosystems, such integrative exploration of DBF relationships would require too extensive manipulations and observations over unfeasible spatial and temporal scales. We argue that microbial systems offer a bright perspective to overcome these limitations, and present a roadmap for doing so. Microbial systems allow us exposing different, well characterized communities to multiple, reproducible disturbance regimes, and precisely measuring both biodiversity and associated functions over time. Comprehensive data can be obtained by systematically varying and replicating representative environmental scenarios. These data can further be explored and explained with computational models. Microbial systems thus reveal mechanisms that underlie DBF relationships and allow scrutinizing ecological hypotheses. This advancement of theory will be essential for ecology as a whole. It is particularly relevant in the context of global change, which is expected to promote disturbances as well as loss of biodiversity and functions in many ecosystems

One major interest in soil systems ecology is to maintain ecosystem functions. As soil is exposed to disturbances of different spatial configurations, identifying disturbance characteristics that still allow for maintaining functions is crucial. In macro-ecology, the influence of fragmentation on ecosystems is continuously debated, especially in terms of extinction thresholds on the landscape scale. Whether this influence is positive or negative depends on the considered type of fragmentation: habitat fragmentation often promotes population extinction, whereas spatially fragmented disturbances reduce extinction probability in many cases. In this study, we make use of these concepts to analyze how spatial disturbance characteristics determine functional resilience on the microscale. We used the numerical model eColony considering bacterial growth, substrate consumption, and dispersal for analyzing the dynamic response of biodegradation as an exemplary important microbial ecosystem function to disturbance events. We systematically varied the frequency of the disturbance events, and the size and fragmentation of the disturbed area. We found that the influence of the disturbance size on functional recovery depends on the spatial fragmentation of the disturbance, indicating that to some extent disturbance size can be compensated for by the spatial configuration of the disturbed area. In general, biodegradation performance decreases as the disturbed area increases in size, and becomes more contiguous. However, if a disturbance is highly fragmented, an increase in disturbance size has no influence on biodegradation performance unless the disturbance is critically large. In this case, the functional performance decreases dramatically. Under recurrent disturbances, this critical disturbance size is shifted toward lower values depending on the disturbance frequency. Our results indicate the importance of spatial disturbance characteristics for functional resilience of soil mi

Trait variation within populations is an important area of research for empirical and theoretical ecologists. While differences between individuals are doubtlessly ubiquitous, their role for species coexistence is much less clear and highly debated. Both unstructured (random) and structured (linked to space, time or inheritance) intraspecific trait variation (ITV) may modify species interactions with nontrivial consequences for emerging community compositions. In many ecosystems, these compositions are further driven by prevalent disturbance regimes. I therefore explored the effects of unstructured as well as spatially structured ITV under disturbances in a generic ecological model of competing sessile species. Using spatially explicit, individual‐based simulations, I studied how intraspecific variation in life history traits together with interspecific trade‐offs and disturbance regimes shape long‐term community composition. I found that 1) unstructured ITV does not affect species coexistence in the given context, 2) spatially structured ITV may considerably increase coexistence, but 3) spatially clumped disturbances reduce this effect of spatially structured ITV, especially if interspecific tradeoffs involve dispersal distance. The findings suggest that spatially structured ITV with individual trait responses to local habitat conditions differing among species may create or expand humps in disturbance–diversity relationships. Hence, if present, these forms of spatially structured ITV should be included in ecological models and will be important for reliably assessing community responses to environmental heterogeneity and change.

Fungi and bacteria often share common microhabitats. Their co-occurrence and coevolution give rise to manifold ecological interactions in the mycosphere, here defined as the microhabitats surrounding and affected by hyphae and mycelia. The extensive structure of mycelia provides ideal “logistic networks” for transport of bacteria and matter in structurally and chemically heterogeneous soil ecosystems. We describe the characteristics of the mycosphere as a unique and highly dynamic bacterial habitat and a hot spot for contaminant biotransformation. In particular, we emphasize the role of the mycosphere for (i) bacterial dispersal and colonization of subsurface interfaces and new habitats, (ii) matter transport processes and contaminant bioaccessibility, and (iii) the functional stability of microbial ecosystems when exposed to environmental fluctuations such as stress or disturbances. Adopting concepts from ecological theory, the chapter disentangles bacterial–fungal impacts on contaminant biotransformation in a systemic approach that interlinks empirical data from microbial ecosystems with simulation data from computational models. This approach provides generic information on key factors, processes, and ecological principles that drive microbial contaminant biotransformation in soil. We highlight that the transport processes create favorable habitat conditions for efficient bacterial contaminant degradation in the mycosphere. In-depth observation, understanding, and prediction of the role of mycosphere transport processes will support the use of bacterial–fungal interactions in nature-based solutions for contaminant biotransformation in natural and man-made ecosystems, respectively.

Bacterial degradation of organic compounds is an important ecosystem function with relevance to, e.g., the cycling of elements or the degradation of organic contaminants. It remains an open question, however, to which extent ecosystems are able to maintain such biodegradation function under recurrent disturbances (functional resistance) and how this is related to the bacterial biomass abundance. In this paper, we use a numerical simulation approach to systematically analyze the dynamic response of a microbial population to recurrent disturbances of different spatial distribution. The spatially explicit model considers microbial degradation, growth, dispersal, and spatial networks that facilitate bacterial dispersal mimicking effects of mycelial networks in nature. We find: (i) There is a certain capacity for high resistance of biodegradation performance to recurrent disturbances. (ii) If this resistance capacity is exceeded, spatial zones of different biodegradation performance develop, ranging from no or reduced to even increased performance. (iii) Bacterial biomass and biodegradation dynamics respond inversely to the spatial fragmentation of disturbances: overall biodegradation performance improves with increasing fragmentation, but bacterial biomass declines. (iv) Bacterial dispersal networks can enhance functional resistance against recurrent disturbances, mainly by reactivating zones in the core of disturbed areas, even though this leads to an overall reduction of bacterial biomass.

Terrestrial microbial ecosystems are exposed to many types of disturbances varying in their spatial and temporal characteristics. The ability to cope with these disturbances is crucial for maintaining microbial ecosystem functions, especially if disturbances recur regularly. Thus, understanding microbial ecosystem dynamics under recurrent disturbances and identifying drivers of functional stability and thresholds for functional collapse is important. Using a spatially explicit ecological model of bacterial growth, dispersal, and substrate consumption, we simulated spatially heterogeneous recurrent disturbances and investigated the dynamic response of pollutant biodegradation – exemplarily for an important ecosystem function. We found that thresholds for functional collapse are controlled by the combination of disturbance frequency and spatial configuration (spatiotemporal disturbance regime). For rare disturbances, the occurrence of functional collapse is promoted by low spatial disturbance fragmentation. For frequent disturbances, functional collapse is almost inevitable. Moreover, the relevance of bacterial growth and dispersal for functional stability also depends on the spatiotemporal disturbance regime. Under disturbance regimes with moderate severity, microbial properties can strongly affect functional stability and shift the threshold for functional collapse. Similarly, networks facilitating bacterial dispersal can delay functional collapse. Consequently, measures to enhance or sustain bacterial growth/dispersal are promising strategies to prevent functional collapses under moderate disturbance regimes.

Fungal–bacterial interactions are highly diverse and contribute to many ecosystem processes. Their emergence under common environmental stress scenarios however, remains elusive. Here we use a synthetic microbial ecosystem based on the germination of Bacillus subtilis spores to examine whether fungal and fungal-like (oomycete) mycelia reduce bacterial water and nutrient stress in an otherwise dry and nutrient-poor microhabitat. We find that the presence of mycelia enables the germination and subsequent growth of bacterial spores near the hyphae. Using a combination of time of flight- and nanoscale secondary ion mass spectrometry (ToF- and nanoSIMS) coupled with stable isotope labelling, we link spore germination to hyphal transfer of water, carbon and nitrogen. Our study provides direct experimental evidence for the stimulation of bacterial activity by mycelial supply of scarce resources in dry and nutrient-free environments. We propose that mycelia may stimulate bacterial activity and thus contribute to sustaining ecosystem functioning in stressed habitats.

Functional stability of microbial ecosystems subjected to disturbances is essential for maintaining microbial ecosystem services such as the biodegradation of organic contaminants in terrestrial environments. Functional responses to disturbances are thus an important aspect which is, however, not well understood yet. Here, we present a microbial simulation model to investigate key processes for the recovery of biodegradation. We simulated single disturbances with different spatiotemporal characteristics and monitored subsequent recovery of the biodegradation dynamics. After less intense disturbance events local regrowth governs biodegradation recovery. After highly intense disturbance events the disturbance pattern's spatial configuration is decisive and processes governing local functional recovery vary depending on habitat location with respect to the spatial disturbance pattern. Local regrowth may be unimportant when bacterial dispersal from undisturbed habitats is high. Hence, our results suggest that spatial dynamics are crucial for the robust delivery of the ecosystem service biodegradation under disturbances in terrestrial environments.