Understanding causal relations for sustainability scientists means studying phenomena that involve complex causality, e.g. multiple and heterogeneous relations and entities, context-sensitivity, and multi-scalar phenomena. To cope with this, sustainability scientists have borrowed concepts from neighboring disciplines, used causal expressions that have confusing meaning, or abstained from using causal language altogether. We argue for using causal language as it is useful for prediction, manipulation, explanatory understanding and responsibility attribution. However, traditional views on causality have limitations dealing with causal complexity. We spell out the challenge of formulating useful concepts. We argue that it is important to recognize the role of everyday causal cognition and its limitations, to distinguish the different ways in which sustainability scholars talk about complexity and to clarify the causal meaning of complexity concepts, like non-linearity, adaptive capacity, and feedback. Finally, we propose the concept of causal configuration to make explicit the causal meaning of complexity-related concepts.

The integrated European Long-Term Ecosystem, critical zone and socio-ecological Research (eLTER) is an emerging pan-European, in-situ Research Infrastructure (RI). Once fully established, it will serve multiple scientific communities with high-level central facilities and distributed well-instrumented eLTER sites. In the Horizon Europe project Biodiversity Digital Twin (BioDT), eLTER already plays the role of a provider for European datasets, in particular for the Grassland Dynamics prototype digital twin. Here, GRASSMIND, an individual- and process-based grassland model designed for simulating the structure and dynamics of species-rich herbaceous communities, including these communities’ responses to climate and management, is to be upscaled to model different local grassland sites across Europe. As the eLTER in-situ site network also comprises such grassland sites, the site registry DEIMS-SDR (deims.org) was used to identify relevant sites and contact the respective site managers and researchers to mobilise data. This selection process was aided by the machine-actionable data endpoints of eLTER also accessible using the Python and R packages, deimsPy and ReLTER, enabling script-based extraction and analysis. Collected and mobilised data is to be published on the persistent data storage B2Share and made centrally accessible through the eLTER central data node. Metadata about the resources is also available in RDF format, making them interlinked and accessible via a SPARQL endpoint. The data provided will enable stronger validation and improvements of the grassland simulations, and thus to better scientific insights and grassland management recommendations.

European grassland management has traditionally prioritized high production through frequent mowing and heavy fertilization, often at the expense of biodiversity conservation, which thrives under less intensive management. With climate change and extreme weather increasingly affecting grassland productivity and biodiversity, adaptive management practises are essential. This project describes the development of a prototype Digital Twin (pDT) for monitoring and projecting grassland biodiversity dynamics under different management and climate scenarios. Grasslands cover approximately 30% of Europe’s agricultural land and are subject to diverse practizes such as grazing, mowing, fertilization, and irrigation. Intensive management often leads to biodiversity loss, whereas less frequent interventions can support a high richness of plant species and contribute to ecosystem resilience. However, climate change further challenges the balancing act between productivity and biodiversity. In this pDT, we employ and further develop GRASSMIND, an individual-based, mechanistic ecological model, to simulate biodiversity dynamics in grasslands. GRASSMIND explicitly models processes such as plant establishment, growth, and mortality at individual levels, providing insights into emergent biodiversity patterns. The project developed a robust data processing pipeline to prepare input data, recalibrate the model, and minimize deviations between simulations and observations. High-performance computing resources, including the LUMI petascale supercomputer, were utilized to parallelize GRASSMIND simulations across hundreds of cores. Data on plant species composition (plus additional data on site conditions and grassland dynamics if available) from different eLTER sites is used as input data and observational data for the GRASSMIND model. The data is provided following the FAIR principles (Findable, Accessible, Interoperable, Reusable) as defined by the project requirements. The data provision workflow builds on the eLTER IT infrastructure and complies with eLTER data best practices. This research highlights the potential of integrating ecological modeling, advanced computing, and open data standards to address critical questions in biodiversity and ecosystem management.

AbstractIn this chapter we start the discussion about causal idiom by giving excerpts from three papers, each discussing the dynamics of a social-ecological system. There is plenty of talk about causes in these papers, but, interestingly, the authors talk about causes and effects without much reflection on the criteria for something being a cause of something else, nor about the required evidence for such claims.

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.

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.

The use of mechanistic population models as research and decision-support tools in ecology and ecological risk assessment (ERA) 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 (CMDs), and both terminology and content vary widely. Here, we argue that greater consistency in CMD 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 ERAs. Integr Environ Assess Manag 2024;20:1566-1574. © 2023 SETAC.

AbstractThe book has so far introduced fundamental ideas about causation, i.e., the relation between cause and effect, from philosophy, particularly those ideas that underlie studies of causation based on quantitative data and statistical methods of causal inference (Chaps. 1–7). Knowledge of these concepts, ideas and associated methods is essential as they are often used in sustainability science studies rooted in the natural sciences, economics and other quantitative social sciences. The book has also introduced the notions of causal explanation and causal mechanisms, which are used more broadly in both quantitative and qualitative studies to explain how a cause brings about an effect (Chap. 8). In this last chapter we want to reflect on causal reasoning from a broader angle, to illustrate the diversity of ways in which sustainability researchers reason about causation, and to highlight the many instances within a research process in which researchers engage in causal reasoning.

In their response letter, Gascoigne et al. propose a relevant approach to characterizing ecological buffer mechanisms, akin to the study of buffer mechanisms in chemistry [1]. Their chemistry-inspired viewpoint enables them to pinpoint opportunities for further advances in the population buffering framework. We welcome the authors' response and concur with their belief that ecology stands to gain significantly from increased crosstalk with chemistry and other more mechanistic, first-principles-driven fields of natural sciences.

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.

AbstractThere are several forms of explanation, one of which being causal explanations. Causal explanations are often descriptions of mechanisms, i.e., descriptions of how a state change in one object, labelled ‘the cause’, is transmitted through a number of intermediate objects to the final effect, i.e., a state change in another object. So the fundamental structure of mechanistic explanations is that of chained cause-effect relations.The main points of this chapter are: Causal explanation is one kind of explanation beside several other kinds. A causal explanation often consists of describing the mechanism by which the cause produces its effect. Reasons for human actions are often viewed as causes of those actions, but that is controversial. Three types of causal explanations in terms of mechanisms are confounder mechanisms, feedbacks, and bifurcations.

Sustainability researchers aim to generate knowledge about causes of societal problems and possiblesolutions. Given the multidisciplinary nature of the field and the complexity of the problems, thecausal reasoning that underlies these activities may vary significantly across studies and researchapproaches. Causal reasoning involves many assumptions, e.g. about what aspects of a systemmatter, what counts as evidence for a causal claim or what biases the data. These assumptionsinfluence the emerging causal understandings, yet they are rarely made explicit. We clarify when andhow causal reasoning manifests during a research process and how it is shaped by the goals of astudy and the underlying idea of causation. Drawing on philosophy of science and recent discussionsin the social and natural sciences, we identify four fundamental ideas and illustrate and comparethem through examples. Awareness of these differences’ influence on causal reasoning helps betterevaluate causes and solutions and identify synergies to strengthen causal claims on complexsustainability problems.

Social-ecological systems (SES) research is a field in which interdisciplinary collaboration is necessary to understand the causal complexity of phenomena such as biodiversity loss and climate change. However, interdisciplinarity poses challenges for evaluation of evidence and causal claims, which is a crucial for generating and integrating knowledge, prediction and informing policy. A single hierarchy of evidence is not possible when research approaches confront different practical and theoretical challenges. We argue that a way forward to evaluate claims is an analysis of causal argumentation, which is an understudied aspect of causation in SES research. Analysis of causal argumentation traces the reasoning linking data and causal claims, discloses assumptions, and makes explicit the support of robustness and generality that qualify claims. We demonstrate this approach by surveying a selection of the field’s most cited papers from the last ten years to analyse the justificatory strategies used to make causal claims. We found that claims in this small corpus are diverse and arguments rely on combinations of reasoning patterns, like inference to the best explanation, mechanism-based thinking, and Mill’s method of difference. There is no causal claim that has a general scope, high strength, and captures causal complexity. Arguments that better captured causal complexity supplemented the argument with causal models that are visually represented. Triangulation of multiple evidence produced claims about more aspects of causal configurations, and achieved more specificity, and stronger claims. Studies that dealt with smaller and simpler causal configurations were able to triangulate evidence more easily. Most of the studies could benefit from considering alternative explanations. We conclude that analysing causal argumentation helps to recognise the limitations of our strategies, make our arguments sharper, and decide what information is useful for decision making.

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.

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 individualbased 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.

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 stageand 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 late summer and early autumn, they were severely affected in cold regions; and (5) in autumn, the populations were equally affected across all regions. The duration and start of each phase differed slightly depending on the climate change scenario and simulation period, but overall, they showed the same pattern. Our model can be used to identify regions of concern and devise management recommendations. The model can be adapted to the life cycle of different target species, climate projections and disturbance regimes. We show with our adaption of the HiLEG model that high-resolution PVAs and applications on large environmental gradients can be reconciled to develop conservation strategies capable of dealing with multiple stressors.