SESMO seeks to transform society and socio-environmental decision-making through model-based research that integrates multiple issues, domain expertise and interest groups
International Environmental Modelling and Software Society
From guinea pigs to guides: advancing lessons learned from reviewing end-to-end marine ecosystem models
Holistic approaches to managing living marine resources, which consider the suite of ecological and environmental interactions affecting a population, are becoming increasingly common. Often, these approaches utilize predictive models that include ecosystem dynamics. However, the application of marine ecosystem modeling tools is generally limited due to the lack of formal reviews of the model's utility and performance, despite these practices being commonplace for single-species stock assessment models. Herein, we provide an account of our experience undergoing a formal review of a Gulf of Mexico end-to-end marine ecosystem model. Guided by lessons learned from the “guinea pigs'' preceding us, described by Kaplan and Marshall (2016), we crafted and implemented a two-phase project timeline consisting of an informal review with regional experts and a formal review with independent experts. While the outcome of our review was that the model was not yet ready for use, a list of necessary model refinements provided by the reviewers offered a clear path for the model toward operational use. We reflect upon the practical challenges, successes, and setbacks encountered during our experience, offering insights into structuring a marine ecosystem model review for future applications. Additionally, building upon previous recommendations, we provide a list of baseline standards for reviewing marine ecosystem model performance. Addressing the inherent challenges in the review of marine ecosystem models is crucial for unlocking their potential contributions to ecosystem-based management, and our recommendations outlined herein offer guidance for future reviews.
Joint Special Issue on Good Modelling Practice
Copyright (c) 2025 Holly Perryman, Cameron H. Ainsworth, Michelle Masi, Isaac C. Kaplan, Howard Townsend, Skyler R. Sagarese, Matthew A. Nuttall, Rebecca L. Scott, Hallie C. Repeta http://creativecommons.org/licenses/by-nc/4.0
Applying an ethical lens for more responsible modelling practice
In recent years there has been an increasing emphasis on including humans when modelling socio-environmental systems. However, it is crucial that we remain mindful of the impacts that the decisions made during model development or analysis can have on people or nature as modelling is not an impartial process. Responsible modelling requires us to consider the broader societal implications of our work, therefore, modellers should consider a range of ethical concerns, often found beyond those prescribed through institutionally mandated ethical approval processes. Herein we examine the ethical dimensions of six socio-environmental case studies using the principles of credibility, legitimacy, and salience, encompassing the modelling process from conception to delivery and beyond. We also discuss the results from an interdisciplinary workshop held with experienced modellers to co-produce a list of ethical dimensions that modellers would ideally engage with when conducting a modelling project. Based on our findings, we have developed a set of recommendations to: i) support modellers in ensuring their modelling practice is underpinned by ethical reflection, ii) guide end-users of model outputs when selecting and repurposing those outputs, and iii) identify means by which institutions can support responsible modelling practices. Engaging with ethical dimensions in the process of modelling is critical for building trust with stakeholders, therefore enhancing the credibility, legitimacy, and salience of the models and research.
Copyright (c) 2025 Katrina Szetey, Delphi Ward, Sabrina Chakori, David Douglas, Elizabeth Fulton, Nicola Grigg, Emma Ligtermoet, Claudia Munera-Roldan, Esther Onyango, Erinne Stirling, Roshni Subramaniam http://creativecommons.org/licenses/by-nc/4.0
Scenario search: finding diverse, plausible and comprehensive scenario sets for complex systems
Complex systems such as cities, energy grids, or the global climate have many plausible futures. Scenarios, or structured narratives of decision-relevant futures, are a common decision support tool for making the complexity and uncertainties of complex systems humanly interpretable. However, the effectiveness of scenario-based decision support depends in part on the usefulness of the selected scenarios. Here we show an optimization-based approach for generating scenarios that are specifically designed to be diverse, plausible, and comprehensive. We establish the advantages of our method by evaluating it against three previously proposed methods: scenario matrices, generic archetypes, and clustering. Our case study is Schelling’s segregation model, a tractable yet behaviorally rich simulation of a complex system. Our results show the proposed optimization-based approach can generate more diverse, plausible, and comprehensive scenarios than existing approaches. The resulting scenarios may provide a more insightful and robust basis for policy decisions, especially for complex systems with emergent behavior or where substantial uncertainties are present.
Copyright (c) 2025 Patrick Steinmann, Judith Verstegen, George van Voorn, Sabin Roman, Arend Ligtenberg http://creativecommons.org/licenses/by-nc/4.0
Solving the challenges of interpolating NO2 from SPRINT data and modelling population movements in agent-based modelling
This study addresses two critical challenges in urban air quality exposure simulation and offers solutions. The first challenge is to generate nitrogen dioxide (NO2) fields across the city of London from available stations that provide Spatially Poor but Rich In Time (SPRINT) data. We first used Inverse Distance Weighting (IDW) to spatially interpolate NO2 at each half-a-day step. Each station had a list of hourly NO2 values for each time step, from which one NO2 value was selected by a stochastic process to generate the field. We also added weightings of up to a factor of 3 to London's NOx emissions to account for emissions from sources other than vehicles. We cross-validated the modelled data with the station data and found beta parameter of 1.5 to be the most appropriate 'power' parameter. The second challenge investigated the use of a fractional origin-destination (OD) matrix to see how to overcome errors when assigning destinations to a small set of population. We tested that ‘the nested bin strategy’ worked well for our 6,078 London resident agents. To enrich the dynamics to represent people’s non-work mobility patterns, we included visits to recreational areas during weekends and festive periods. This, in turn, provides a more comprehensive representation of urban mobility. Solutions to each challenge can provide more accurate assessments of pollution exposure, leading to better informed public health interventions.
spatially poor but rich in time (SPRINT)
Copyright (c) 2024 Hyesop Shin, Eric Silverman http://creativecommons.org/licenses/by-nc/4.0
Community-based monitoring in tropical environments: Establishing indigenous participation without direct western involvement
This article constitutes a socio-technical exploration of how community forest monitoring operates and circulates within indigenous and tropical contexts. Authored collaboratively by a western anthropologist and two indigenous cartographer-activists from Panama, this study meticulously explores the pathways through which a novel form of geospatial monitoring, based on high-tech, lightweight and easy-to-handle instruments, finds its way into indigenous strategies for territorial control and defense. It offers an in-depth insight into the endeavors of committed indigenous cartographers who are actively engaged in the dissemination of an autonomously-led monitoring device in Central America and contributes to the still relatively limited body of literature on the topic. This study sets aside a results-oriented approach, focusing instead on analyzing basic processes of technological appropriation in indigenous communities. We demonstrate how, despite the diversity of cultural and ecological contexts across our case studies, the socio-technical system we propose proved to be similarly implemented, precisely because it manages to bring into tension technologies with indigenous geographical knowledge and practices.
Special Issue: Indigenous & Local Perspectives in Citizen Science and Participatory Modelling
Copyright (c) 2025 Yann Voisin, Carlos Doviaza, Eliceo Quintero http://creativecommons.org/licenses/by-nc/4.0
“Knowledge Strength”: maximising the reliability of evidence derived from environmental modelling in the face of uncertainty – the case of the salmon louse (Lepeophtheirus salmonis)
Policy developments for a sustainable Blue Economy require scientific advice. An important component of the Blue Economy is salmon (Salmo salar) aquaculture, the sustainability of which is limited by salmon louse (Lepeophtheirus salmonis) infection. This parasite impacts both farmed and wild salmonid fish. Modelling is a valuable source for advice, but inevitable uncertainties exist. Here we develop an approach we call “Knowledge Strength” to maximise our confidence in model results; this is aimed at reducing uncertainties in model outputs and understanding the remaining uncertainty in these outputs, to maximise our confidence in model results, so that we can give policy makers the best advice to support informed decision making. The approach consists of addressing five questions: (1) What is the objective addressed by the model? (2) What are the causes of uncertainty in model outputs? We describe uncertainties due to (a) limitations of computing, (b) model building and (c) parameters, and (d) forcing data. (3) What is the statistical nature of uncertainty? Noise and bias are qualitatively different. (4) How can knowledge strength be maximised given those uncertainties? Approaches of resourcing (“power”) and analysis (“wisdom”) are considered. (5) How can information, including uncertainties, be communicated to different audiences? Policy makers/managers define and resource the objective for question 1, modellers address questions 2 to 4 - but their solutions are made transparent, and the communication question 5 is a two-way process with outputs transparent to immediate decision makers and external stakeholders. Examples of the policy environment behind salmon lice management are detailed in the Supplementary Material covering Scotland, Norway and the Faroe Islands.
Copyright (c) 2025 Alexander Murray, Lars Asplin, Gunnvor a Nordi, Sissal Erenbjerg, Alejandro Gallego, Stephen C Ives, Erin King, Trondur Kragesteen, Joanne Murphy, Berit Rabe, Anne D Sandvik, Jofrid Skardhamar , Meadhbh Moriarty http://creativecommons.org/licenses/by-nc/4.0
Mediated modeling lessons for stakeholder engagement with science-based management: case study focused on Texas Gulf Coast (USA) estuaries
In this paper, we illustrate the use of mediated modeling and collaborative learning as a framework for good modeling practice. We describe facilitatation of local stakeholder involvement in developing policy recommendations for managing important estuaries along the Texas Gulf Coast. We designed a shared system of learning among local stakeholders and scientists, and via a series of workshops integrated that shared learning into a quantitative computer model. The quantitative model that emerged focused on estuary components of greatest interest to participants. Workshop participants initially developed a conceptual model of two adjacent Texas Gulf Coast (USA) estuaries, which are part of the Mission Aransas National Estuarine Reserve. We then formalized the conceptual model into a quantitative model representing the spatial-temporal dynamics of one of the estuaries. Engagement in this collaborative modeling process enabled workshop participants to understand more fully what is known, suggest ways to fill important knowledge gaps, and to experiment with the quantitative model to project possible futures for the selected estuary.
Copyright (c) 2025 Paulami Banerjee, Hsiao-Hsuan (Rose) Wang, Chara J. Ragland, William E. Grant, Tarla R. Peterson http://creativecommons.org/licenses/by-nc/4.0
How to use the impossible map – Considerations for a rigorous exploration of Digital Twins of the Earth
The term “Digital Twins of the Earth” has rocketed into scientific use and policymaker discourse by promising a virtual replica of our planet. While the potential of a digital representation of reality is captivating for environmental monitoring, decision-making, and scientific inquiry, the term lacks a clear and shared definition and may be misleading. It conceals that all digital representations are models and, as such, will always be detached from reality. Detailed simulation models are excellent digital laboratories that allow us to interrogate our theories about the world in ways otherwise not possible, given the limited scales at which we can run real-world experiments, yet a perfect representation of reality is impossible as it would be exactly as complex. As we embark on the journey of building such detailed models, one question we must ask is, "How can we ensure that they can be explored with scientific rigor?" Here, we discuss possible ways to utilize a model's internal variability to understand its dominant controls to increase our understanding of both the models we build and the world that they represent.
Copyright (c) 2024 Robert Reinecke, Francesca Pianosi, Thorsten Wagener http://creativecommons.org/licenses/by-nc/4.0
Promoting scientific software quality through transition to continuous integration and continuous delivery
Software quality has been an issue for decades in many areas of scientific modelling for environment applications. Much of the software that has been developed is well-suited to supporting research investigation and application in one-off curated application environments, such as delivering solutions through community-based participatory approaches. However, when releasing the software into constrained production environments, with everyday operational challenges such as missing data, unintended user inputs, variable data quality, and values outside the bounds of those previously tested, lacks in software quality can become apparent. The lacks can result in lost time and effort to get the model running again, loss of trust in the model by users and, potentially, cessation in model use, and early and unexpected end-of-life. There have been many efforts through the years to encourage improved software quality, through approaches such as test-driven development, paired programming, more rigorous documentation, and better and broader user acceptance testing. The basis of many of these approaches is the art and science of software testing, through the whole development chain from unit testing, regression testing, integration testing, functional testing and to user testing. Continuous Integration/ Continuous Development (CI/CD)/ Continuous Deployment uses an automated or semi-automated process pipeline that promotes or progresses software through various stages of 'readiness' in different system realms, from development to testing to production. Faithful application of CI/CD promotes good quality software by requiring the software to pass a series of pre-determined tests before it is accepted into the next realm. Models must meet quality levels and pass automated tests to be able to be promoted. Additionally, governance gateways are used to check non-automated workflow components, such as manual testing. This paper explores the challenges and lessons learned in adoption and application of secure CI/CD in an operational environmental modelling enterprise, and suggests a minimum viable good practice approach for application to scientific environmental modelling.
Copyright (c) 2024 Dr Anja Schubert, Dr Robert Argent http://creativecommons.org/licenses/by-nc/4.0
Community-informed Decisions for Equitable, Cost-effective, and Inclusive Disaster Resilience Planning (Co-DECIDR): A modeling approach
Evaluating coping strategies for community resilience planning in the face of natural hazards and disasters presents significant challenges, including limited system clarity, diverging community priorities, resource inequalities, and inherent uncertainties. Traditional methods — often reliant on economic metrics through benefit-cost analysis — primarily assess monetary aspects, overlooking critical non-monetary factors. The increasing shift towards transdisciplinary methods underscores the importance of integrating local community values into resilience planning. This research introduces the Co-DECIDR (Community-informed Decisions for Equitable, Cost-effective, and Inclusive Disaster Resilience) modeling framework, designed to evaluate resilience planning alternatives by embracing community-identified values, leveraging local insights, navigating uncertainties, and balancing monetary with non-monetary considerations. By merging Fuzzy Cognitive Mapping (FCM) for qualitative depth with Benefit-Cost Analysis (BCA) for quantitative rigor, Co-DECIDR enables a comprehensive understanding of complex social-ecological systems. The framework utilizes accessible online tools — Mental Modeler for FCM and Economic Decision Guide Software (EDGe$) for BCA — to enhance user engagement and model efficacy. Demonstrated through proof-of-concept examples in Flint, Michigan, focusing on resilience to extreme weather events and pandemics like COVID-19, this study showcases Co-DECIDR’s practicality and adaptability in real-world contexts. These examples highlight the framework's capacity to support informed, community-centric, and equitable resilience planning. Furthermore, Co-DECIDR advances good modeling practice by utilizing participatory modeling in resilience planning to bridge the gap between modelers and end-users. It systematically captures stakeholder requirements, investigates subjectivity, addresses collaborative modeling challenges, encourages the engagement of diverse groups, and adopts a comprehensive approach to assessing model performance, embracing both qualitative and quantitative criteria.
Copyright (c) 2025 Mahdi Zareei, Carissa Knox, Jennifer Helgeson, Steven A. Gray, Richard C. Sadler, Laura Schmitt Olabisi, Chelsea Wentworth http://creativecommons.org/licenses/by-nc/4.0
Model evaluation: The misuse of statistical techniques when evaluating observations versus predictions
Mathematical modellers, decision support developers, statisticians, and students evaluate the differences between observed and model predicted values. When evaluating models, it is far too easy to conduct model evaluation by fitting a linear regression to the data. In this paper, steps are presented on ‘how to’ evaluate a model using deviance metrics rather than reporting r2 from fitting a linear regression. The paper aims to provide sound reasoning, with data, against using r2. The paper addresses five arguments, previously put forward, for not fitting a linear regression when conducting model evaluation: i) Misapplication of regression; ii) Ambiguity of null hypothesis tests; iii) Lack of sensitivity; iv) Fitted line is irrelevant to validation; and v) Violation of regression assumptions. Statistical, deviance, and quality control metrics are outlined. Three models using the BeefSpecs drafting tool are reported in this paper. Each model (n = 80) had an r2 of 0.43. A mean bias of 0.06, -2.90, and -0.11 mm, and a root mean square error of prediction (RMSEP) of 1.72, 3.37, and 3.70 mm for models 1, 2, and 3, respectively. A modelling efficiency (MEF) of 0.39, -1.34, and -1.83, and 91, 51, and 56% of predictions within upper and lower quality control limits for models 1, 2, and 3, respectively. These metrics highlight the pitfall of reporting r2 from using regression. Minimum recommended steps of ‘how to’ conduct model evaluation are: a plot of the residuals with quality control limits and a table of metrics including mean observed, predicted and bias, RMSEP, and MEF.
Copyright (c) 2024 Malcolm McPhee, Jonathan Richetti, Barry Croke, Brad Walmsley http://creativecommons.org/licenses/by-nc/4.0
The complexity of the food transition: a social simulation perspective
This paper first discusses the complexity of a transition in the food system, taking the stance of a system-of-systems approach where cascading effects can happen. A complexity perspective is taken in explaining transitional dynamics and their limited predictability. Following that, we focus on the social and behavioural dimensions of transitions. Here we zoom in on human needs as fundamental drivers, explaining how tensions between different needs may lay at the root of individual and social tipping points. Agent based modelling is explained as a methodology to simulate these social and behavioural dynamics, offering a tool to address the human factor into modelling the food system-of-systems. It is discussed that such agent-based models should integrate different behavioural drivers and processes to be capable of modelling transitional dynamics.
Copyright (c) 2024 Wander Jager http://creativecommons.org/licenses/by-nc/4.0
Eliciting psychosocial factors using search query data to improve conceptual modeling: A case study of Japanese food supply chains during the COVID-19 pandemic
To effectively address environmental and social issues such as climate change and sustainability, it is necessary to build a conceptual model that allows stakeholders with different perceptions and values to reach a consensus and make decisions toward achieving the desired goals. The construction of conceptual models using participatory modeling methods involves exploring psychosocial factors that cause stakeholders to act, and physical variables such as people’s activities and environmental events. However, many of these processes rely heavily on qualitative data obtained from interviews. This results in a lack of modeling transparency and excessive complexity. Psychosocial factors also tend to be treated as exogenous variables. In this study, we propose a participatory modeling method that uses qualitative interview data and quantifiable keyword search data to explore the psychosocial factors that should be incorporated into the conceptual model. These psychosocial factors are key components of stakeholders’ mental models. This method identifies characteristic search queries by considering changes in the quantity and time series of search queries, and combines interview data and distinct search queries to create a causal loop diagram in collaboration with stakeholders. Incorporating psychosocial factors as endogenous variables into the conceptual model increases the model’s reliability and enables an understanding of the potential for complex nonlinear dynamics across social and environmental dimensions. We examined the applicability of this process using a case study that explored changes in eating habits and intervention points in Tokyo before and after the COVID-19 pandemic.
Copyright (c) 2025 Akinori Komaki, Mizuho Sato, Madoka Nakajima, Naohiko Kohtake http://creativecommons.org/licenses/by-nc/4.0
Towards normalizing good practice across the whole modeling cycle: its instrumentation and future research topics
Choices made in modeling matter and demand more explication since they determine how much we can trust modeling insights and predictions within their social, political and ethical contexts. Good Modeling Practice (GMP) is a key research area for strengthening and maturing the modeling field and community, through identifying, formulating and sharing knowledge about the craft of modeling. This craft represents the knowledge that modelers learn in practice about how they get things done, and how they adapt their practices to new situations. This Joint Special Issue is motivated by the importance of sharing good modeling practices from a whole modeling lifecycle viewpoint. We attempt to add conceptual clarity to this research area by defining the plethora of concepts and decision points used to characterize the choices to be made throughout the modeling process, and by synthesizing some of the existing efforts on GMP. We characterize a broad list of articles in the literature on GMP and identify a list of essential topics demanding more attention. This list is only a preliminary one as we anticipate that a more comprehensive list of knowledge gaps will be unearthed from the submissions to the Joint Special Issue collection on GMP, of which this is an introduction. We also propose a vision for GMP and suggest instrumental ways that good practice can become not just well-known but normal practice. This instrumentation focuses on journal standards, collective commitment and culture especially by research community societies, early career awards for advancing GMP, and legal requirements or accreditation. A vital instrument in all this is the design and development of a modeling curriculum that distills core requisite knowledge about modeling, as well as proven-to-work routines and practices that can be scaled up in different contexts.
Copyright (c) 2024 Anthony J. Jakeman, Sondoss Elsawah, Hsiao-Hsuan Wang, Serena H. Hamilton, Lieke Melsen, Volker Grimm http://creativecommons.org/licenses/by-nc/4.0
Increasing behavioral richness and managing structural uncertainty in social-ecological system agent-based models
Responding to the challenges of societal transformation in the face of climate change, efforts to integrate behaviorally rich models of adaptation decision-making into large-scale macroeconomic and Earth system models are growing and agent-based models (ABMs) are an effective tool for doing so. However, behavioral richness in ABMs has been limited to implementations of single decision models for all agents in a simulated population. The main goals of this study were to: 1) implement the ‘building-block processes’ (BBPs) approach for decision model heterogeneity; 2) demonstrate the application of sensitivity and uncertainty analyses to quantify the scope of structural uncertainty produced by alternative decision models under variable price and climate conditions; and 3) apply the Observing System Simulation Experiment (OSSE) approach to validate such a behaviorally rich BBPs model at the level of individual agent decisions. Using an ABM of agricultural producers’ decision-making, we demonstrated that uncertainty in crop and farm management decisions introduced by heterogeneous decision models was equal to and in some instances greater than that due to variable price or precipitation conditions. Unrealistically rapid or stagnant behavioral dynamics were evident in model versions implementing single decision models for all agents. Moreover, interactions among agents with diverse decision models in the same population produced consistently more accurate outcomes and realistic behavioral dynamics. The BBPs framework and accompanying sensitivity and uncertainty analyses demonstrated here offer a path forward for increasing behavioral richness in ABMs, which is key to understanding processes of adaptation central to societal responses to climate change.
Copyright (c) 2024 Nicholas Magliocca, Ruchie Pathak, Ashleigh Price, Hashir Tanveer, Mukesh Kumar, Hamid Moradkhani http://creativecommons.org/licenses/by-nc/4.0
Model documentation in the eye of the beholder: Lessons learned from a flood risk model for a dike in the Netherlands
Computer models are essential for flood risk decision-making, where documentation plays a key role in ensuring transparency and reproducibility. This study examined how different stakeholders perceive documentation practices by analysing a Dutch flood-risk model case study. We assessed perspectives from external observers (ourselves), the modellers (a consultancy company), and the model end-users (the Water Authority) through documentation analysis, model reruns, and interviews. We found a mismatch between the Water Authority’s documentation goals and what was achieved with the delivered documentation. For instance, we could not reproduce the model results based on the available documentation, as crucial tacit knowledge remained implicit. Despite these shortcomings, both the consultancy company and the Water Authority were satisfied with the final products — illustrating how documentation quality is in the eye of the beholder and in this case shaped by the trust from the Water Authority in the consultancy company. However, when key goals like transparency and reproducibility are not met, accountability for model results becomes a concern, especially in decision-making contexts — highlighting the gap between perceived adequacy and broader expectations of documentation.
Copyright (c) 2025 Brit E. van der Meijden and Lieke Melsen http://creativecommons.org/licenses/by-nc/4.0
Robust strategies for forest wildfire mitigation under uncertainty
Modern forest management is often focused on minimizing wildfire risk by regaining fire-resiliency from historical conditions. This process is labor and resource intensive and it is usually infeasible to treat entire forests. Therefore, forest treatment allocation optimization is applied to help identify the best mitigation plan, with a key objective being the minimization of departure from historical landscape conditions. However, the vagueness and imprecision of historical landscape condition models and their corresponding departure indicators due to data limitations often necessitate the use of simulated substitutes. Since spatial optimization, integral for identifying optimal mitigation plans, relies on simulation outputs, addressing uncertainty becomes a crucial concern. We analyze optimization outputs for different historical condition scenarios and propose strategies to identify robust alternatives in two steps. First, we conduct a series of spatial optimizations using a weighted-sum method, systematically varying the importance assigned to different objectives for each scenario of historical conditions. This process helps us understand how changes in the assumptions about past forest conditions and the relative weights assigned to each assumption affect the optimal management plans. Second, we analyze the resulting solutions to identify patterns and overlaps among the optimal spatial configurations across scenarios. By focusing on the identification of common patterns, we develop a strategy to select solutions that are robust to uncertainty in historical condition models. We find that the strategy based on identifying commonalities among optimal solutions yields management plans that are, on average, 8.9% more robust compared to plans derived without explicit consideration of robustness. We conclude that the commonality seeking strategy in solution space alleviates risk in decision making. These insights highlight the importance of developing strategies for applying robust strategies in spatially explicit optimization problems.
historical range and variation simulation
Copyright (c) 2025 Moritz Hildemann, Amelia B. Pludow, Alan T. Murray, Edzer Pebesma, Judith A. Verstegen http://creativecommons.org/licenses/by-nc/4.0
Developing multidisciplinary mechanistic models: challenges and approaches
Current biodiversity models often struggle to represent the complexity of global crises, as the affected ecosystems are shaped by many different ecological, physical, and social processes. To understand these dynamics better, we will need to build larger and more complex ecological models, and couple ecological models to models produced by other disciplines, such as climate science, economics, or sociology. However, constructing such integrated models is a significant technical undertaking, which has received little attention by ecological modellers so far. We review literature from computer science and several other environmental modelling disciplines to identify common challenges and possible strategies when creating large integrated models. We show that there is a software-architectural trade-off between modularity and integration, where the former is required to keep the technical complexity of a model manageable, and the latter is desirable to represent the scientific complexity of a studied system. We then present and compare five different software engineering techniques for navigating this trade-off. Which technique is most suitable for a given model depends on the model’s aims and the available development resources. The larger a model becomes, the more important it is to use more advanced techniques, such as integrating models from different domains using a model coupling framework. Our review shows that ecological modellers can learn from other modelling disciplines, but also need to invest in increased software engineering expertise, if they want to build models that can represent the numerous processes affecting ecosystems and biodiversity loss.
Copyright (c) 2024 Daniel Vedder, Samuel M. Fischer, Kerstin Wiegand, Guy Pe'er http://creativecommons.org/licenses/by-nc/4.0
An overview of variance-based importance measures in the linear regression context: comparative analyses and numerical tests
One of the most fundamental issues in many socio-environmental studies is the identification of causal effects and influential variables related to phenomena of interest. In the context of regression analysis, importance measures are effective tools for feature selection and model interpretation, allowing for the ranking of the most influential regressors. In particular, variance-based importance measures (VIMs) are a prominent topic in the field of statistics, as well as in the emerging field of global sensitivity analysis. This is due to their accessible interpretation as variance shares of the explained variable. This work focuses on the linear regression model and aims to provide an updated overview of the most well-founded methods, mainly from comparative analyses and numerical tests on various toy cases. The paper also addresses some of the practical challenges that arise, including the case of dependent inputs and high input dimensionality. The practical relevance of these tools is demonstrated through empirical studies on simulated data and public datasets. The Supplementary Material C also presents the use of VIMs in a classification context, specifically via the logistic linear regression model.
Special Issue: Sensitivity Analysis of Model Output
Copyright (c) 2025 Bertrand Iooss, Laura Clouvel, Vincent Chabridon, Marouane Il Idrissi, Frédérique Robin http://creativecommons.org/licenses/by-nc/4.0
The Collaborative Policy Modeling Paradox: Perceptions of water quality modeling in the Chesapeake Bay Watershed
The Chesapeake Assessment Scenario Tool (CAST) serves multiple key functions in meeting nutrient reduction targets across the Chesapeake Bay Watershed (CBW) and is embedded in the water quality governance system. To investigate contested perspectives regarding the model, we interviewed 59 stakeholders engaged in model governance across the CBW. We recorded statements regarding the accuracy, legitimacy, and credibility of the model, influences on its use, and on challenges and opportunities. We found skepticism regarding the legitimacy of CAST, including suggestions its role facilitates a “paper process” of policy design and that past experience has greater influence on policy decisions than model predictions. However, despite its perceived shortcomings, CAST has been central in helping stakeholders in prioritizing mitigative activities. With respect to credibility, most respondents believe the model underestimates the effects of nutrient-reduction practices, thereby underestimating progress toward TMDL-related goals. Respondents also identified opportunities for model improvement, emphasizing co-benefits of conservation practices over and above nutrient reduction. Overall, our analysis demonstrates a Collaborative Policy Modeling Paradox: collaborative model development is necessary for effective policy modeling, but the political processes of collaborative model development can negatively impact perceptions of salience, credibility, and legitimacy. Although it is important to recognize this paradox, as it is linked to dissatisfaction with the models, our findings also point to areas where improvement has occurred and to future opportunities for development.
Special Issue: SES Modeling in Regulatory Contexts
Copyright (c) 2024 Patrick Bitterman, D. G. Webster http://creativecommons.org/licenses/by-nc/4.0
Power to the programmer: Modeller’s perspective on automating the setup of hydrodynamic models for Dutch water authorities
Use of models in decision making, for example in water management, requires confidence in the model and its outputs. Since choices in model setup affect model output, this confidence is affected by the modellers’ professional judgement. Computer programmers can use their expertise in coding to standardise some of the tasks associated with computational modelling. Therefore, centralized automation has the potential to ensure quality of modelling decisions. Since it is the modeller that makes the choices in the model set-up, it is important to understand how modellers perceive automation. To explore their perspectives, we conducted fourteen interviews with modellers at water authorities and consulting companies in the Netherlands. The transcripts were analysed through deductive and inductive content analysis. Our study reveals that automated modelling processes can improve efficiency, transparency and consistency, but only if certain requirements are met, such as good documentation, clear ownership, adequate maintenance, and frequent evaluation. Therefore, managing the risks and benefits of automation requires balancing the power between modellers and programmers.
Copyright (c) 2024 J.O.E. Remmers, A.J. Teuling, R.J. Dahm, A. van Dam, L.A. Melsen http://creativecommons.org/licenses/by-nc/4.0
Interactive visual syntheses for social-ecological systems understanding
Many social-ecological systems are in an unsustainable state. Bringing together disjunct published findings on complex interactions in social-ecological systems may enable the identification of leverage points for transformations towards sustainability. However, such interdisciplinary synthesis studies on specific regional social-ecological systems remain rare. Here, we pair a review of systematically identified studies with a cross-impact analysis to create an interactive visual social-ecological systems synthesis on conflicts and synergies between land use, biodiversity conservation, and human wellbeing in north-eastern Madagascar. The interactive visual synthesis (https://visualsynthesis.wyssacademy.org) depicts an archetypical regional landscape with 22 factors comprising physical landscape elements, ecosystem services, wellbeing, human activities, and telecouplings. To understand the connections between these factors, we assess directional causal links based on literature sources. The visual synthesis shows that research has so far focused on links between land use and biodiversity while links to human wellbeing were studied more seldomly. We then identify chains and cycles that emerge from the links between factors and rate them based on their plausibility and relevance. All eight top-rated chains and cycles relate to subsistence and commercial agriculture, revealing promising leverage points at which interventions could improve outcomes for biodiversity and wellbeing. In sum, we show how interactive visual syntheses can be a useful way to make disjunct published findings on regional social-ecological systems more accessible, to find research gaps, and to identify leverage points for sustainability transformations.
Special Issue: Participatory & cross-scale modelling of SESs in the Anthropocene
Copyright (c) 2024 Dominic A. Martin, Myriam Pham-Truffert, Lara Gillham, O. Ravaka Andriamihaja, R. Ntsiva N. Andriatsitohaina, Clara L. Diebold, Thio Rosin Fulgence, Elke Kellner, Jorge Claudio Llopis, Peter Messerli, Anjaharinony A. N. A. Rakotomalala, Estelle Raveloaritiana, Annemarie Wurz, Julie G. Zaehringer, Andreas Heinimann http://creativecommons.org/licenses/by-nc/4.0
Assessing convergence in global sensitivity analysis: a review of methods for assessing and monitoring convergence
In global sensitivity analysis (GSA) of a model, a proper convergence analysis of metrics is essential for ensuring a level of confidence or trustworthiness in sensitivity results obtained, yet is somewhat deficient in practice. The level of confidence in sensitivity measures, particularly in relation to their influence and support for decisions from scientific, social and policy perspectives, is heavily reliant on the convergence of GSA. We review the literature and summarize the available methods for monitoring and assessing convergence of sensitivity measures based on application purposes. The aim is to expose the various choices for convergence assessment and encourage further testing of available methods to clarify their level of robustness. Furthermore, the review identifies a pressing need for comparative studies on convergence assessment methods to establish a clear hierarchy of effectiveness and encourages the adoption of systematic approaches for enhanced robustness in sensitivity analysis.
Copyright (c) 2024 Xifu Sun, Anthony J. Jakeman, Barry F.W. Croke, Stephen G. Roberts, John D. Jakeman http://creativecommons.org/licenses/by-nc/4.0
Reflections on SES modeling: Stop me if you’ve heard this
Key lessons about and limits to social-ecological systems (SES) modeling are widely available and frustratingly consistent over time. Prominent challenges include outdated perspectives about systems and models along with persistent disciplinary hegemony. The inherent complexity in SES means that an emphasis on discrete prediction is misplaced and has potentially reduced model efficacy for decision-making. Although computer models are definitely the tool to use to identify the complex relationships within SES, humans are messy and hence the ‘social’ in SES is often ignored, glossed over, or reduced to simplistic economic or demographic variables. This combination of factors has perpetuated biases in what is worth pursuing and/or publishing. In (re)visiting issues in SES modeling, including debates about model capabilities, data selection, and challenges in working across disciplinary lines, this reflection explores how the author’s experience aligns with extant literature as well as raises issues about what is absent from that body of work. The available lessons suggest that scholars and practitioners need to re-think how, why, and when to employ SES modeling in regulatory or other decision-making contexts.
Copyright (c) 2024 Kristan Cockerill http://creativecommons.org/licenses/by-nc/4.0
Global sensitivity analysis of the dynamics of a distributed hydrological model at the catchment scale
The PESHMELBA model simulates water and pesticide transfers at the catchment scale. Its objective is to help the process of decision making in the common management of long-term water quality. Performing the global sensitivity analysis (GSA) of this type of model is necessary to trace the output variability to the input parameters. The goal of the present work is to perform a GSA, while considering the spatio-temporal nature and the high dimensionality of the model. The output considered is the surface moisture simulated over a two-month period on a catchment of assorted mesh elements (plots). The GSA is performed on the dynamical outputs, rewritten through their functional principal components. Sobol’ indices are then estimated through polynomial chaos expansion on each principal component. The analysis differs between the two types of behaviour observed in the surface moisture outputs. The hydrodynamic properties of the surface soil have a dominant influence on the average surface moisture. Nonetheless, the parameters describing deeper soil layers influence the output dynamics of those plots where the surface moisture is saturated. We obtain Sobol’ indices with high precision while using a limited number of model estimations and considering the models spatio-temporal nature. The physical interpretation of the GSA confirms and augments our knowledge on the model.
Copyright (c) 2023 Katarina Radišić, Emilie Rouzies, Claire Lauvernet, Arthur Vidard http://creativecommons.org/licenses/by-nc/4.0
Global sensitivity analysis of a one-dimensional ocean biogeochemical model
Ocean biogeochemical (BGC) models are a powerful tool for investigating ocean biogeochemistry and the global carbon cycle. The potential benefits emanating from BGC simulations and predictions are broad, with significant societal impacts from fisheries management to carbon dioxide removal and policy-making. These models contain numerous parameters, each coupled with large uncertainties, leading to significant uncertainty in the model outputs. This study performs a global sensitivity analysis (GSA) of an ocean BGC model to identify the uncertain parameters that impact the variability of model outputs most. The BGC model Regulated Ecosystem Model 2 is used in a one-dimensional configuration at two ocean sites in the North Atlantic (BATS) and the Mediterranean Sea (DYFAMED). Variance-based Sobol' indices are computed to identify the most influential parameters for each site for the quantities of interest (QoIs) commonly considered for the calibration and validation of BGC models. The most sensitive parameters are the chlorophyll to nitrogen ratio, chlorophyll degradation rate, zooplankton grazing and excretion parameters, photosynthesis parameters, and nitrogen and carbon remineralization rate. Overall, the sensitivities of most QoIs were similar across the two sites; however, some differences emerged because of different mixed layer depths. The results suggest that implementing multiple zooplankton function types in BGC models can improve BGC predictions. Further, explicitly implementing heterotrophic bacteria in the model can better simulate the carbon export production and CO2 fluxes. The study offers a comprehensive list of the most important BGC parameters that need to be quantified for future modeling applications and insights for BGC model developments.
Copyright (c) 2023 Nabir Mamnun, Christoph Völker, Sebastian Krumscheid, Mihalis Vrekoussis, Lars Nerger http://creativecommons.org/licenses/by-nc/4.0
Cross-scale feedbacks and tipping points in aggregated models of socio-ecological systems
Many researchers have called for more consideration of cross-scale dynamics in models of socio-ecological systems, but this is a fundamentally difficult thing to do. Focussing on cross-scale feedbacks and tipping points, this paper uses three example models to demonstrate and reflect on how cross-scale dynamics can be incorporated into aggregate models. Tipping points - where a small perturbation can lead to a qualitative change in a system - are generally the result of nonlinear feedback mechanisms. These feedback mechanisms often operate on different levels within or across scales. Tipping points occurring on one level or scale may cascade across to others. Interest in these types of cross-scale feedbacks and tipping points is high, but consideration of how to model them is underdeveloped. The representation of cross-scale feedbacks and tipping points in aggregated models of socio-ecological systems remains a critical challenge for modellers, with implications for the types of models and policy advice that can be developed. We present three case studies to demonstrate and reflect on how cross-scale feedbacks and tipping points can be represented and analysed in these models. Two key themes emerge from our reflections: (i) the variety and trade-offs in ways to explore and present model behaviour, using tools such as scenario analysis and phase portraits; and (ii) the subjectivity inherent in considering and implementing scale, in aggregated models.
Copyright (c) 2024 Peter Barbrook-Johnson, George van Voorn, Hsiao-Hsuan Wang, Fateme Zare, William E. Grant, Zach Posnik, Melvin Lippe http://creativecommons.org/licenses/by-nc/4.0
Participation in modeling social-ecological systems across scales in agriculture and forestry
Participatory approaches have gained recognition in scientific research, particularly in modeling human-environmental interactions and social-ecological systems (SES). Despite this achievement, a lack of in-depth understanding prevails to which degree, and at which scales and levels, participation takes place during the modeling cycle. Furthermore, information on the linkages between the level of the participating stakeholders and the modeling level of agents and environmental processes remains scarce. To shed light on this research gap, we conducted a systematic review including 79 relevant studies focusing on social-ecological systems within agricultural or forestry domains. Results reveal that participation was mostly carried out at lower spatial levels (local and regional) while stakeholders commonly originated from one spatial level, mainly the study area. Agent behavior was mainly simulated at a single spatial level. The number of studies modeling multi- and cross-scale levels or integrating participation was small. In rare cross-level modeling cases, local-level agents were predominately simulated as an aggregate at higher levels. We found that stakeholders most commonly participated in model parameterization and calibration, but rarely in model results communication. Participation was also relatively strong in model design, development and validation. This can be explained by the need of scientists for model input or to improve the transparency of the modeling process. To address these gaps in future studies, we recommend to model agents operating at their “real-world” level, while capturing interactions between agents across different scales, rather than (dis)aggregate agent behaviors to a single level. Furthermore, participation should go beyond fulfilling the mere scientific requirement of model development, and should be used to enhance the applicability and relevance of SES models.
Copyright (c) 2025 Markus Meyer, Yue Dou, Hedwig van Delden, Thanh Thi Thi Nguygen, Melvin Lippe http://creativecommons.org/licenses/by-nc/4.0
Modelling agricultural innovations as a social-ecological phenomenon
Agricultural innovations involve both social and social-ecological dynamics where outcomes emerge from interactions of innovation actors embedded within their ecological environments. Neglecting the interconnected nature of social-ecological innovations can lead to a flawed understanding and assessment of innovations. In this paper, we present an empirically informed, stylized agent-based model of agricultural innovation systems in Mali, West Africa. The study aimed to understand the emergence of food security and income inequality outcomes through two distinct model structures: top-down, aid-driven (exogenous) innovation and bottom-up, community-driven (endogenous) innovation. Our research questions were: i) How does the inclusion of social-ecological interactions in the model affect food security and income inequality outcomes? ii) How do exogenous and endogenous mechanisms influence food insecurity and income inequality? iii) What are the conditions under which exogenous and endogenous mechanisms would improve food security? The structural design of the model was based on a combination of theory, empirics, and mapping of social-ecological dynamics within innovation systems. Using the Social-Ecological Action Situation framework, we mapped the social, social-ecological, and ecological interactions that jointly produce food security outcomes. The exploratory model analysis reveals three key insights: i) Incorporation of social-ecological interactions influences model outcomes. Scenarios with social-ecological interactions showed a stronger relationship between income inequality and food security, lower levels of food security, and higher levels of income inequality than scenarios with social interactions. ii) Endogenous mechanism leads to higher food security and income inequality than the exogenous mechanism. iii) Bidirectional outreach is more effective than unidirectional outreach in improving food security. Inclusion of social-ecological dynamics and interactions such as the role of climate risk perception, social learning and formation of innovation beliefs and desires is key for modelling and analysis of agricultural innovations.
Copyright (c) 2023 Udita Sanga, Jineth Berrío-Martínez and Maja Schlüter‬ http://creativecommons.org/licenses/by-nc/4.0
Chimaera Modelling – when the modellers must reconcile inconsistent elements or purposes
Socio-Ecological System modelling projects are becoming increasingly complicated, with multiple actors and aspects being the norm. Such projects can cause problems for the modellers when this involves different elements, goals, philosophies, etc., all pulling in different directions – we call this “Chimaera Modelling.” Although such situations are common when you talk to modellers, they do not seem to be explicitly discussed in the literature. In this paper, we attempt to turn this perceived “inside” phenomenon into an “outside” phenomenon and to start a debate to increase transparency among the modelling community. We discuss the different aspects which may be relevant to this problem to start this debate, including: the underlying philosophy, modelling goals, extent of choice the modellers have, different stages of modelling, and kinds of actors that are involved. We further map out some of the dimensions with which Chimaera Modelling connects. We briefly discuss these and propose to the community as a whole to work on their methodological development, feasibility, risks and applicability as their resolution is far beyond the scope of this paper. We end with a brief description of the broad possible approaches to such situations. Our main message is a call for recognition of Chimaera Modelling as a likely side-effect of multi-stakeholder, multi-purpose projects, and to take this into account proactively at the project team level and be transparent about the tensions and contradictions that underly such modelling.
Copyright (c) 2024 Bruce Edmonds, Gert Jan Hofstede, Jennifer Koch, Christophe le Page, Theo Lim, Melvin Lippe, Beatrice Nöldeke, Hedwig van Delden http://creativecommons.org/licenses/by-nc/4.0