Identifying uncertainties in scenarios and models of socio-ecological systems in support of decision-making

Abstract

There are many sources of uncertainty in scenarios and models of socio-ecological systems, and understanding these uncertainties is critical in supporting informed decision-making about the management of natural resources. Here, we review uncertainty across the steps needed to create socio-ecological scenarios, from narrative storylines to the representation of human and biological processes in models and the estimation of scenario and model parameters. We find that socio-ecological scenarios and models would benefit from moving away from “stylized” approaches that do not consider a wide range of direct drivers and their dependency on indirect drivers. Indeed, a greater focus on the social phenomena is fundamental in understanding the functioning of nature on a human-dominated planet. There is no panacea for dealing with uncertainty, but several approaches to evaluating uncertainty are still not routinely applied in scenario modeling, and this is becoming increasingly unacceptable. However, it is important to avoid uncertainties becoming an excuse for inaction in decision-making when facing environmental challenges. There are many sources of uncertainty in scenarios and models of socio-ecological systems, and understanding these uncertainties is critical in supporting informed decision-making about the management of natural resources. Here, we review uncertainty across the steps needed to create socio-ecological scenarios, from narrative storylines to the representation of human and biological processes in models and the estimation of scenario and model parameters. We find that socio-ecological scenarios and models would benefit from moving away from “stylized” approaches that do not consider a wide range of direct drivers and their dependency on indirect drivers. Indeed, a greater focus on the social phenomena is fundamental in understanding the functioning of nature on a human-dominated planet. There is no panacea for dealing with uncertainty, but several approaches to evaluating uncertainty are still not routinely applied in scenario modeling, and this is becoming increasingly unacceptable. However, it is important to avoid uncertainties becoming an excuse for inaction in decision-making when facing environmental challenges. “The whole problem with the world is that fools and fanatics are always so certain of themselves, but wiser people so full of doubts.”1Russell B. History of Western Philosophy. Simon & Schuster, 1945Google Scholar With this phrase, Bertrand Russell highlights the imperative of embracing uncertainty rather than fooling ourselves into thinking that it does not exist. This holds especially true for how we understand the natural world, including the increasingly important role of humans in socio-ecological systems. We know that socio-ecological systems are complex. They are non-linear, bifurcate, and have feedbacks and tipping points, all of which makes their future development inherently uncertain and difficult to predict. Indeed, the future is a place we can never know; we cannot observe it, and we cannot measure it. Yet, decision-makers are challenged with planning short- to long-term strategies for preserving biodiversity and the contributions of nature to people2IPBESThe IPBES regional assessment report on biodiversity and ecosystem services for Europe and Central Asia.in: ). IPBES, 2018Google Scholar and, so, we need to anticipate what the future may hold. The scientific response to this challenge has been the development of scenarios to explore the uncertainty space of plausible, but unknown, futures.3IPBESThe methodological assessment report on scenarios and models of biodiversity and ecosystem services. IPBES, 2016Google Scholar Scenarios are not predictions, but are “a plausible and often simplified description of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces and relationships.”4Millennium Ecosystem AssessmentEcosystems and Human Well-Being: Synthesis. Island Press, 2005Google Scholar Scenarios are commonly underpinned by qualitative descriptions (narrative storylines) of the underlying direct and indirect drivers of change, including policy options,3IPBESThe methodological assessment report on scenarios and models of biodiversity and ecosystem services. IPBES, 2016Google Scholar,5Rounsevell M.D.A. Metzger M.J. Developing qualitative scenario storylines for environmental change assessment.Wiley Interdiscip. Rev. Clim. Change. 2010; 1: 606-619Google Scholar which are often translated into impacts on biodiversity, ecosystem services, and complex socio-ecological systems using models in a storyline and simulation approach.3IPBESThe methodological assessment report on scenarios and models of biodiversity and ecosystem services. IPBES, 2016Google Scholar Hence, scenarios can be qualitative, quantitative, or both. As such, scenarios and models are invaluable tools in guiding long-term, strategic policies that prompt management actions and increase public awareness of the future threats to nature.6Acosta L.A. Wintle B.A. Benedek Z. Chhetri P.B. Heymans S.J. Onur A.C. Painter R.L. Razafimpahanana A. Shoyama K. Using scenarios and models to inform decision making in policy design and implementation.in: Ferrier S. Ninan K.N. Leadley P. Alkemade R. Acosta L.A. Akçakaya H.R. Brotons L. Cheung W.W.L. Christensen V. Harhash K.A. 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Evol. 2008; 23: 338-346Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar but, until now, far less effort has been dedicated to exploring the uncertainties within these models, especially when used to quantify scenarios. Identifying and quantifying future uncertainties may be key in achieving buy-in from stakeholders, to prompt evidence-based decision-making, and to shift mindsets on the perception of the future threats to biodiversity, ecosystems, and ecosystem services. To increase the influence of scenario and modeling analyses on policy and to trigger appropriate management responses, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has strongly encouraged the use of scenarios and models, but warns that these “should be applied with care, taking into account uncertainties and unpredictability associated with model-based projections.”3IPBESThe methodological assessment report on scenarios and models of biodiversity and ecosystem services. IPBES, 2016Google Scholar A critical challenge for improving scenarios and models of socio-ecological systems is to augment the scientific capacity in quantifying the uncertainty within and among model projections.18Pereira H.M. Leadley P.W. Proença V. Alkemade R. Scharlemann J.P.W. Fernandez-Manjarrés J.F. Araújo M.B. Balvanera P. Biggs R. Cheung W.W.L. et al.Scenarios for global biodiversity in the 21st century.Science. 2010; 330: 1496-1501Crossref PubMed Scopus (1085) Google Scholar Here, we review the current state of knowledge about the uncertainties associated with scenarios and models of socio-ecological systems within the context of decision-making, by which we mean the policy decisions made within private or public sector organizations. In doing so, we seek to address some of the key challenges raised by Elsawah et al.19Elsawah S. Hamilton S.H. Jakeman A.J. Rothman D. Schweizer V. Trutnevyte E. Carlsen H. Drakes C. Frame B. Fu B. et al.Scenario processes for socio-environmental systems analysis of futures: a review of recent efforts and a salient research agenda for supporting decision making.Sci. Total Environ. 2020; 729: 138393https://doi.org/10.1016/j.scitotenv.2020.138393Crossref Google Scholar that relate to uncertainty, such as the role of stakeholder engagement in the co-development of scenarios, linking scenarios across multiple geographical, sectoral, and temporal scales, improving the links between qualitative and quantitative scenarios, addressing surprises, addressing scenario consistency, communicating scenarios, and linking scenarios to decision-making. We do not aim to undertake an exhaustive evaluation of scenarios and model types. Instead, we use examples from a very wide range of scenarios and models to illustrate a comprehensive review of sources of uncertainty. A comprehensive review of sources of uncertainty in scenarios and models does not require a comprehensive review of scenarios and models. A wider ranging review can be found in the IPBES3IPBESThe methodological assessment report on scenarios and models of biodiversity and ecosystem services. IPBES, 2016Google Scholar assessment of scenarios and models. We provide an overview of how uncertainty is treated within socio-ecological systems analysis and how understanding these uncertainties can enhance confidence in the creation of the next generation of scenarios and models. This is novel in both tackling a comprehensive review of sources of uncertainty in scenarios and models, exploring the implications of these uncertainties for decision-making and in setting out a number of potential solutions and recommendations for how to deal with these uncertainties. We focus on three categories of uncertainty: scenario uncertainty, model uncertainty, and decision-making uncertainty (see Table 1) across terrestrial and marine realms. We explore the whole chain of steps needed to create socio-ecological scenarios and models that are useful for decision-makers, from narrative storylines, the representation of human and biological processes in models, the estimation of model parameters, and model initialization and evaluation. Some of these sources of uncertainty relate to differences in worldviews, some to the limits of our current knowledge and others to our capacity to represent processes within models, including the reliability of model input data across spatial and temporal scales. Figure 1 shows the types of uncertainty (from Table 1) in the steps from observational data, model development, the construction of qualitative storylines and quantitative scenario projections that together provide input to decision-making.Table 1Sources of uncertainty and their description in scenarios and models of socio-ecological systemsUncertainty sourcesDescriptionUncertainty typesScenario uncertaintyThe qualitative description of alternative worldviews and their development into the future and the quantification of model input parameters that are conditional on these descriptions.Linguistic uncertainty. The use of similar terms to mean different things in different research communities, e.g., pathways, ensembles, boundary conditions.Narratives storyline uncertainty. The limits to imagining unknown futures (e.g., unknown unknowns). This can relate, for example, to alternative worldviews or the uncertainties associated with participatory processes arising from internal consistency and knowledge limitations.Scenario parameter uncertainty. The estimation of quantitative parameters from narrative storylines that are subsequently used in models. Scenario parameter uncertainty follows from the interpretation of quantitative values from qualitative narratives, e.g., the number of people in a “high population growth” scenario.Model uncertaintyThe representation of processes in models and how this is done.Structural (epistemic) uncertainty. The uncertainties associated with the choice and the representation of processes in models.Input data uncertainties. The variability in baseline data conditions that are used to initialize a model, including thematic classification, i.e., how classes are defined in, for example, land-use maps.Error propagation uncertainty. The amplification (or dampening) of the transmission of errors across multiple coupled models. The role of meta-modeling and indirect effects (such as cross-sectoral interactions).Decision uncertaintyCommunicating and translating the results of scenario and modeling studies into decision-making.Data interpretation for decision-making. Selective use of data or information from different sources and their interpretation.Analyzing at relevant spatiotemporal scales. The selection of spatiotemporal scales at which simulated data are analyzed, and the granularity of derived indicators (e.g., level of integration across biodiversity facets, merging subsets of ecosystem services).Decision-making tools. The variety of decision-supporting methods, e.g., multi-criteria decision analysis. Open table in a new tab Linguistic uncertainty has been classified into five distinct types: vagueness, context dependence, ambiguity, indeterminacy of theoretical terms, and under-specificity.20Regan H.M. Colyvan M. Burgman M.A. A taxonomy and treatment of uncertainty for ecology and conservation biology.Ecol. Appl. 2002; 12: 618-628Crossref Google Scholar Of these, ambiguity and vagueness arguably occur most commonly, largely because scenario terminology is often based on common language words. Indeed, the word “scenario” itself derives from the language of the theater. Yet, different communities can sometimes attribute different meanings to the same “precise” word, i.e., their use is ambiguous. For example, the word “pathways” is used as a synonym for “projections” or “trajectories” (as in the shared socio-economic pathways),21O’Neill B.C. Kriegler E. Riahi K. Ebi K.L. Hallegatte S. Carter T.R. Mathur R. van Vuuren D.P. A new scenario framework for climate change research: the concept of shared socioeconomic pathways.Climatic Change. 2014; 122: 387-400Crossref Scopus (0) Google Scholar or alternatively it is used to describe a set of time-dependent actions that are required to achieve a future vision.2IPBESThe IPBES regional assessment report on biodiversity and ecosystem services for Europe and Central Asia.in: ). IPBES, 2018Google Scholar Using the term in one sense can lead to confusion if it is interpreted as being used in the other sense. Vagueness relates to statements with insufficient precision. For example, “population growth will increase strongly over the coming 50 years” tells us nothing about what a strong population growth actually looks like. Is it a doubling of population, or tripling, or something else? These different types of linguistic uncertainty commonly occur in narrative storylines, and they are especially important considerations when communicating the outcomes of scenario processes to decision-makers. Recent development of information technology provides a means to minimize linguistic uncertainty by building ontologies, i.e., an ensemble of formal definitions of concepts and their relationships within the domain of interest, and their synonyms or equivalents in closely related domains. While domain-specific ontologies exist in ecology that facilitate data mining and sharing,22Madin J.S. Bowers S. Schildhauer M.P. Jones M.B. Advancing ecological research with ontologies.Trends Ecol. Evol. 2008; 23: 159-168Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar to our knowledge, there is no widely accessible controlled vocabulary or thesaurus standardizing the meaning of the basic concepts used in scenarios of socio-ecological systems, as is the case with ontologies related to the Intergovernmental Panel on Climate Change (IPCC).23Sleeman J. Finin T. Halem M. Ontology-grounded topic modeling for climate science research.arXiv. 2018; (arXiv:1807.10965v2)Google Scholar The first step in the construction of scenarios is often the development of qualitative, narrative storylines.5Rounsevell M.D.A. Metzger M.J. Developing qualitative scenario storylines for environmental change assessment.Wiley Interdiscip. Rev. Clim. Change. 2010; 1: 606-619Google Scholar These describe alternative trajectories in the key drivers of change (and their interactions) with a focus on socio-economic change. Socio-economic trajectories can also be associated with changes in physical conditions, such as climate change, where a change in climate is assumed to be internally consistent with drivers of, for example, societal consumption patterns and industrialization.24van Vuuren D.P. Kriegler E. O’Neill B.C. Ebi K.L. Riahi K. Carter T.R. Edmonds J. Hallegatte S. Kram T. Mathur R. et al.A new scenario framework for climate change research: scenario matrix architecture.Climatic Change. 2014; 122: 373-386Crossref Scopus (0) Google Scholar The uncertainties associated with the development of narrative storylines arise from how to create this internal consistency using mental models,25Metzger M.J. Schröter D. Leemans R. Cramer W. A spatially explicit and quantitative vulnerability assessment of ecosystem service change in Europe.Reg. Environ. 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Projecting the effects of climate and socio-economic changes on the global skipjack tuna fisheries under various management strategies.Glob. Environ. Change. 2016; 41: 1-12Google Scholar, 36Maury O. Campling L. Arrizabalaga H. Aumont O. Bopp L. Merino G. Squires D. Cheung W. Goujon M. Guivarch C. et al.From shared socio-economic pathways (SSPs) to oceanic system pathways (OSPs): building policy-relevant scenarios for global oceanic ecosystems and fisheries.Glob. Environ. Change. 2017; 45: 203-216Google Scholar Terrestrial studies have a longer tradition of evaluating multiple, often cross-scale drivers in developing narrative storylines.37Harrison P.A. Dunford R.W. Holman I.P. Rounsevell M.D.A. Climate change impact modelling needs to include cross-sectoral interactions.Nat. Clim. Change. 2016; 6: 885-890Google Scholar However, uncertainties arise from an overreliance on climate change as a driver, and not accounting for other drivers that are critical for socio-ecological systems, such as invasive alien species, trade in wild species, or air and water pollution.2IPBESThe IPBES regional assessment report on biodiversity and ecosystem services for Europe and Central Asia.in: ). IPBES, 2018Google Scholar Furthermore, uncertainties also arise from failure to account for indirect, cross-sectoral interactions.37Harrison P.A. Dunford R.W. Holman I.P. Rounsevell M.D.A. Climate change impact modelling needs to include cross-sectoral interactions.Nat. Clim. Change. 2016; 6: 885-890Google Scholar Participatory approaches, by which narrative storylines are co-created with stakeholders, add richness and diversity to storyline development, and strengthen the link between storylines and scenario quantification with models,38Kok K. Bärlund I. Flörke M. Holman I. Gramberger M. 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Sandø A.B. et al.A participatory scenario method to explore the future of marine social-ecological systems.Fish Fish. 2019; 20: 434-451Google Scholar,40Gopnik M. Fieseler C. Cantral L. McClellan K. Pendleton L. Crowder L. Coming to the table: early stakeholder engagement in marine spatial planning.Mar. Pol. 2012; 36: 1139-1149Google Scholar Simulation models can quantify the outcomes of narrative storylines for specific indicators. This requires the translation of the qualitative statements within a storyline into quantitative model inputs, which in itself has potential to introduce additional uncertainties.5Rounsevell M.D.A. Metzger M.J. Developing qualitative scenario storylines for environmental change assessment.Wiley Interdiscip. Rev. Clim. Change. 2010; 1: 606-619Google Scholar We draw a distinction here between “scenario parameter uncertainty” and “model parameter uncertainty.” Scenario parameter uncertainty derives from the translation of qualitative narratives into quantitative values, and so is dependent on the scenario itself, i.e., the quantitative values vary across scenarios. For example, a scenario parameter could be the number of people in a high, medium, or low population growth storyline. In general, scenario parameters relate to the socio-economic components of socio-ecological systems and may themselves be model inputs. Model parameter uncertainty refers to the estimation of parameters within the functions that represent modeled processes, e.g., a rate constant or capacity, and often, but not always, relate to the biophysical components of socio-ecological systems. Hence, model parameter uncertainty depends on the system and the model of that system, and is independent of a scenario. Scenario parameter quantification often uses best-guess estimates that sometimes draw on uncertain, historical analogs. However, the majority of these studies do not account for the uncertainties associated with the process of estimating scenario parameters themselves. A few exceptions to this have defined “credible” parameter ranges,41Pedde S. Kok K. Onigkeit J. Brown C. Holman I. Harrison P.A. Bridging uncertainty concepts across narratives and simulations in environmental scenarios.Reg. Environ. Change. 2019; 19: 655-666Crossref Scopus (16) Google Scholar or have used conditional probabilistic futures me