Decision science for effective management of populations subject to stochasticity and imperfect knowledge

Many species are threatened by human activity through processes such as habitat modification, water management, hunting, and introduction of invasive species. These anthropogenic threats must be mitigated as efficiently as possible because both time and money available for mitigation are limited. For example, it is essential to address the type and degree of uncertainties present to derive effective management strategies for managed populations. Decision science provides the tools required to produce effective management strategies that can maximize or minimize the desired objective(s) based on imperfect knowledge, taking into account stochasticity. Of particular importance are questions such as how much of available budgets should be invested in reducing uncertainty and which uncertainties should be reduced. In such instances, decision science can help select efficient environmental management actions that may be subject to stochasticity and imperfect knowledge. Here, we review the use of decision science in environmental management to demonstrate the utility of the decision science framework. Our points are illustrated using examples from the literature. We conclude that collaboration between theoreticians and practitioners is crucial to maximize the benefits of decision science’s rational approach to dealing with uncertainty.     

Decision Analysis Frameworks for Life-Cycle Impact Assessment

Life‐cycle impact assessments (LCIAs) are complex because they almost always involve uncertain consequences relative to multiple criteria. Several authors have noticed that this is precisely the sort of problem addressed by methods of decision analysis. Despite several experiences of using multipleattribute decision analysis (MADA) methods in LCIA, the possibilities of MADA methods in LCIA are rather poorly elaborated in the field of life‐cycle assessment. In this article we provide an overview of the commonly used MADA methods and discuss LCIA in relation to them. The article also presents how different frames and tools developed by the MADA community can be applied in conducting LCIAs. Although the exact framing of LCIA using decision analysis still merits debate, we show that the similarities between generic decision analysis steps and their LCIA counterparts are clear. Structuring of an assessment problem according to a value tree offers a basis for the definition of impact categories and classification. Value trees can thus be used to ensure that all relevant impact categories and interventions are taken into account in the appropriate manner. The similarities between multiattribute value theory (MAVT) and the current calculation rule applied in LCIA mean that techniques, knowledge, and experiences derived from MAVT can be applied to LCIA. For example, MAVT offers a general solution for the calculation of overall impact values and it can be applied to help discern sound from unsound approaches to value measurement, normalization, weighting, and aggregation in the LCIA model. In addition, the MAVT framework can assist in the methodological development of LCIA because of its well‐established theoretical foundation. The relationship between MAVT and the current LCIA methodology does not preclude application of other MADA methods in the context of LCIA. A need exists to analyze the weaknesses and the strengths of different multiple‐criteria decision analysis methods in order to identify those methods most appropriate for different LCIA applications.     

Practical Decision Methods for Watershed Management

Why are formal statistical methods for risk-based decision-making so seldom used in the practice of watershed management? I contend that complex formal methods, while internally consistent, are often inappropriate to real world decision-making. The primary purpose of risk analysis is to support risk management, and decision methods need to be effective not just in evaluating risk, but also in communicating risk among stakeholders and decision makers. Useful methods must be not only correct, but also readily communicable. Many formal risk-based decision methods have real obstacles to practical application in one of the following areas: (1) many important components of risk that matter to stakeholders are difficult to express in quantitative terms, and any method which turns “fuzzy” information and subjective opinion into hard numbers is prone to be regarded with suspicion; (2) methods which are not understandable and convincing to decision makers have little practical value; (3) a complex formal analysis will be seen as misguided or irrelevant if it does not represent the full spectrum of management goals. This paper compares the process of watershed management with the process of ecological risk assessment, highlighting similarities and key differences. A practical decision method which balances quantitative rigor with ability to communicate to and forge consensus among stakeholders is then outlined with reference to a successful case study.     

Supporting Fisheries Management by Means of Complex Models: Can We Point out Isles of Robustness in a Sea of Uncertainty?

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters.     

Integrating Comparative Risk Assessment with Multi-Criteria Decision Analysis to Manage Contaminated Sediments: An Example for the New York/New Jersey Harbor

Current environmental challenges involve complex assessment and analysis of tradeoffs among differing criteria, expectations, and levels of certainty. Using multi-criteria decision analysis in combination with comparative risk assessment, a systematic and transparent framework can be created to integrate different types and sources of decision-relevant information. Although examples of decision analysis abound in the literature and academic efforts, its use within the practice of risk assessment and environmental management is still being established. This article provides an example of the use of decision analysis study that builds on a previous screening-level, comparative risk assessment of contaminated sediment from the New York/New Jersey harbor area. Using multi-criteria decision analysis, we explored the effect of different criteria weights, utility functions, and cost estimates on the ranking of seven contaminated sediment management alternatives. Values used in weighting decision criteria were surveyed from two interaction sessions with sediment management professionals.     

Multicriteria Bayesian Analysis of Lower Trophic Level Uncertainties and Value of Research in Lake Erie

Human activities have severely disrupted the Lake Erie ecosystem. Recent changes in the structure of the lower trophic level associated with exotic species invasions and reduced nutrient loading have created ecological uncertainties for fisheries management. Decisions that naïvely assume certainty may be different and suboptimal compared to choices that consider uncertainty. Here we illustrate how multiobjective Bayesian decision analysis can recognize the multiple goals of management in evaluations of the effect of ecological uncertainties on management and the value of information from ecological research. Value judgments and subjective probabilities required by the decision analysis were provided by six Lake Erie fishery agency biologists. The Lake Erie Ecological Model was used to project the impacts of each combination of management actions and lower trophic level parameter values. The analysis shows that explicitly considering lower trophic level uncertainties can alter decisions concerning Lake Erie fishery harvests. Of the research projects considered, investigation of goby predation of zebra mussels (Dreissena sp.) and lakewide estimation of secondary production appear to have the greatest expected value for fisheries management. We also find that changes in the weights assigned to management goals affects decisions and value of information more than do changes in probability judgments.     

Decision Analysis Utilizing Data from Multiple Life-Cycle Impact Assessment Methods Part I: A Theoretical Basis

Numerous methodologies for the life-cycle impact assessment (LCIA) step of life-cycle assessment (LCA) are currently in popular use. These methods, which are based on a single method or level of analysis, are limited to the environmental fates, impact categories, damage functions, and stressors included in the method or model. Because of this, it has been suggested within the LCA community that LCIA data from multiple methods and/or levels of analysis, that is, end-point and midpoint indicators, be used in LCA-based decision analysis to facilitate better or, at least more informed, decision making. In this (two-part) series of articles, we develop and present a series of LCA-based decision analysis models, based on multiattribute value theory (MAVT), which utilize data from multiple LCIA methods and/or levels of analysis. The key to accomplishing this is the recognition of what LCIA damage indicators represent with respect to decision analysis, namely, decision attributes and, in most cases, proxy attributes. The use of proxy attributes in a decision model, however, poses certain challenges, such as the assessment of decision-maker preferences for actual consequences that are only known imprecisely because of inherent limits of both LCA and scientific knowledge. In this article (part I), we provide a brief overview of MAVT and examine some of the decision-theoretic issues and implications of current LCIA methods. We illustrate the application of MAVT to develop a decision model utilizing damage indicators from a single LCIA methodology; and, we identify the decision-theoretic issues that arise when attempting to combine LCIA indicators from multiple methods and/or levels of analysis in a single decision model. Finally, we introduce the use in our methodology of constructed attributes to combine related end-point damage indicators into single decision attributes and the concept and evaluation of proxy attributes.     

Research Implications of Science-Informed,Value-Based Decision Making

In ‘Hard’ science, scientists correctly operate as the ‘guardians of certainty,’ using hypothesis testing formulations and value judgements about error rates and time discounting that make classical inferential methods appropriate. But these methods can neither generate most of the inputs needed by decision makers in their time frame, nor generate them in a form that allows them to be integrated into the decision in an analytically coherent and transparent way. The need for transparent accountability in public decision making under uncertainty and value conflict means the analytical coherence provided by the stochastic Bayesian decision analytic approach, drawing on the outputs of Bayesian science, is needed. If scientific researchers are to play the rôle they should be playing in informing value-based decision making, they need to see themselves also as ‘guardians of uncertainty,’ ensuring that the best possible current posterior distributions on relevant parameters are made available for decision making, irrespective of the state of the certainty-seeking research. The paper distinguishes the actors employing different technologies in terms of the focus of the technology (knowledge, values, choice); the ‘home base’ mode of their activity on the cognitive continuum of varying analysis-to-intuition ratios; and the underlying value judgements of the activity (especially error loss functions and time discount rates). Those who propose any principle of decision making other than the banal ‘Best Principle,’ including the ‘Precautionary Principle,’ are properly interpreted as advocates seeking to have their own value judgements and preferences regarding mode location apply. The task for accountable decision makers, and their supporting technologists, is to determine the best course of action under the universal conditions of uncertainty and value difference/conflict.     

Decision errors in evaluating tipping points for ecosystem resilience

When an ecosystem reaches tipping points for selected indicators, resilience to further changes in external drivers can decrease, regime shifts can occur that diminish the capacity of the ecosystem to provide ecosystem services, and the ecosystem is more vulnerable to collapse. Evaluating tipping points for resilience using crisp decision rules can result in decision errors about whether or not resilience has been compromised. The source and nature of those errors are described and a fuzzy decision rule is proposed for evaluating resilience. Decision errors are evaluated for four cases. Cases 1 through 3 (or case 4) derive conditions for evaluating decision errors when there is a single (or multiple) indicator(s). The primary sources of decision errors for the four cases are discrepancies between measured (or established) and true values of the indicators (or tipping points) and using a crisp decision rule to reach conclusions about whether or not resilience has been compromised. A fuzzy decision rule, based on fuzzy TOPSIS, is proposed that evaluates the extent to which an ecosystem is resilient. Although crisp decision rules provide unambiguous conclusions about resilience, those conclusions can be faulty, particularly when measured indicators and established tipping points deviate substantially from their true values. In contrast, the conclusions from the fuzzy decision rule are less susceptible to the decision errors and, hence, faulty decisions.     

Decision analysis for the planning and assessment of ex situ management: An application to the endangered mahogany glider

Ex situ (‘off-site’) management refers to keeping species in artificial conditions away from their natural habitat and includes captive breeding facilities, botanical gardens and seed banks. There is scope for ex situ programmes to be more commonly used for supplementing or establishing wild populations. However, undertaking ex situ management comes with risks, costs and uncertainties, which must be assessed in the context of available in situ (‘on-site’) management options. The PACES (Planning and Assessment for Conservation through Ex situ management) tool tailors the principles of structured decision-making to the specific problem of assessing and comparing ex situ and in situ management options. We applied the PACES tool to the mahogany glider (Petaurus gracilis), a threatened arboreal marsupial endemic to north Queensland, Australia. Through an expert elicitation process, we predicted the likely benefits of an ex situ and two in situ management options, as compared to a baseline ‘do-nothing’ scenario. The ‘in situ plus’ alternative (where extra resources are dedicated to in situ management) was predicted to result in the largest population increase according to the participants' best estimates. However, this benefit came at a much larger cost than the ex situ alternative, and without the benefit of an ex situ insurance population. The PACES tool assessment allowed the Mahogany Glider Recovery Team to document and plan the financial costs, risks and benefits of potential future management options for the mahogany glider, laying a transparent basis for future assessment and decision-making.     

Probabilistic Environmental Risk Assessment for Dibutylphthalate (DBP)

In the risk assessment methods for new and existing chemicals in the European Union (EU), environmental “risk” is characterized by the deterministic quotient of exposure and effects (PEC/PNEC). From a scientific viewpoint, the uncertainty in the risk quotient should be accounted for explicitly in the decision making, which can be done in a probabilistic risk framework. To demonstrate the feasibility and benefits of such a framework, a sample risk assessment for an existing chemical (dibutylphthalate, DBP) is presented in this paper. The example shows a probabilistic framework to be feasible with relatively little extra effort; such a framework also provides more relevant information. The deterministic risk quotients turned out to be worst cases at generally higher than the 95th percentile of the probability distributions. Sensitivity analysis proves to be a powerful tool in identifying the main sources of uncertainty and thus will be effective for efficient further testing. The distributions assigned to the assess ment factors (derivation of the PNEC) dominate the total uncertainty in the risk assessment; uncertainties in the release estimates come second. Large uncertainties are an inherent part of risk assessment that we have to deal with quantitatively. However, the most appropriate way to characterise effects and risks requires further attention. Recommendations for further study are identified.     

Monitoring in the Context of Structured Decision-Making and Adaptive Management

Abstract: In a natural resource management setting, monitoring is a crucial component of an informed process for making decisions, and monitoring design should be driven by the decision context and associated uncertainties. Monitoring itself can play ≥3 roles. First, it is important for state-dependent decision-making, as when managers need to know the system state before deciding on the appropriate course of action during the ensuing management cycle. Second, monitoring is critical for evaluating the effectiveness of management actions relative to objectives. Third, in an adaptive management setting, monitoring provides the feedback loop for learning about the system; learning is sought not for its own sake but primarily to better achieve management objectives. In this case, monitoring should be designed to reduce the critical uncertainties in models of the managed system. The United States Geological Survey and United States Fish and Wildlife Service are conducting a large-scale management experiment on 23 National Wildlife Refuges across the Northeast and Midwest Regions. The primary management objective is to provide habitat for migratory waterbirds, particularly during migration, using water-level manipulations in managed wetlands. Key uncertainties are related to the potential trade-offs created by management for a specific waterbird guild (e.g., migratory shorebirds) and the response of waterbirds, plant communities, and invertebrates to specific experimental hydroperiods. We reviewed the monitoring program associated with this study, and the ways that specific observations fill ≥1 of the roles identified above. We used observations from our monitoring to improve state-dependent decisions to control undesired plants, to evaluate management performance relative to shallow-water habitat objectives, and to evaluate potential trade-offs between waterfowl and shorebird habitat management. With limited staff and budgets, management agencies need efficient monitoring programs that are used for decision-making, not comprehensive studies that elucidate all manner of ecological relationships.     

露天煤矿废弃地生态重建智能决策支持系统的系统分析与总体设计

大型露天煤矿开采引发的土地退化问题复杂,其废弃地的生态重建更是涉及采矿学、地貌学、农学、林学、生态学、环境学、经济学和社会科学等理论。因而,很难用传统的思维方法、数量化和数学模型的应用及研究工具来解决。建立废弃地生态重建智能决策支持系统（Ｉｎｔｅｌｉ...     

Decision matrices: Tools to enhance middle school engineering instruction

Decision matrices are valuable engineering tools. They allow engineers to objectively examine solution options. Decision matrices can be incorporated in K-12 classrooms to support authentic engineering instruction. In this article we provide examples of how decision matrices have been incorporated into 6th and 7th grade classrooms as part of an innovative NGSS-aligned curriculum. The article also provides guidelines and examples for how to scaffold decision matrices to support student and teacher confidence and use.     

Integrating life cycle cost analysis and LCA

The private sector decision making situations which LCA addresses mustalso eventually take theeconomic consequences of alternative products or product designs into account. However, neither the internal nor external economic aspects of the decisions are within the scope of developed LCA methodology, nor are they properly addressed by existing LCA tools. This traditional separation of life cycle environmental assessment from economic analysis has limited the influence and relevance of LCA for decision-making, and left uncharacterized the important relationships and trade-offs between the economic and life cycle environmental performance of alternative product design decision scenarios. Still standard methods of LCA can and have been tightly, logically, and practically integrated with standard methods for cost accounting, life cycle cost analysis, and scenario-based economic risk modeling. The result is an ability to take both economic and environmental performance — and their tradeoff relationships — into account in product/process design decision making.     

A Decision Analytic Approach to Exposure-Based Chemical Prioritization

The manufacture of novel synthetic chemicals has increased in volume and variety, but often the environmental and health risks are not fully understood in terms of toxicity and, in particular, exposure. While efforts to assess risks have generally been effective when sufficient data are available, the hazard and exposure data necessary to assess risks adequately are unavailable for the vast majority of chemicals in commerce. The US Environmental Protection Agency has initiated the ExpoCast Program to develop tools for rapid chemical evaluation based on potential for exposure. In this context, a model is presented in which chemicals are evaluated based on inherent chemical properties and behaviorally-based usage characteristics over the chemical’s life cycle. These criteria are assessed and integrated within a decision analytic framework, facilitating rapid assessment and prioritization for future targeted testing and systems modeling. A case study outlines the prioritization process using 51 chemicals. The results show a preliminary relative ranking of chemicals based on exposure potential. The strength of this approach is the ability to integrate relevant statistical and mechanistic data with expert judgment, allowing for an initial tier assessment that can further inform targeted testing and risk management strategies.     

Incorporating climate change into ecosystem service assessments and decisions: a review

Climate change is having a significant impact on ecosystem services and is likely to become increasingly important as this phenomenon intensifies. Future impacts can be difficult to assess as they often involve long timescales, dynamic systems with high uncertainties, and are typically confounded by other drivers of change. Despite a growing literature on climate change impacts on ecosystem services, no quantitative syntheses exist. Hence, we lack an overarching understanding of the impacts of climate change, how they are being assessed, and the extent to which other drivers, uncertainties, and decision making are incorporated. To address this, we systematically reviewed the peer‐reviewed literature that assesses climate change impacts on ecosystem services at subglobal scales. We found that the impact of climate change on most types of services was predominantly negative (59% negative, 24% mixed, 4% neutral, 13% positive), but varied across services, drivers, and assessment methods. Although uncertainty was usually incorporated, there were substantial gaps in the sources of uncertainty included, along with the methods used to incorporate them. We found that relatively few studies integrated decision making, and even fewer studies aimed to identify solutions that were robust to uncertainty. For management or policy to ensure the delivery of ecosystem services, integrated approaches that incorporate multiple drivers of change and account for multiple sources of uncertainty are needed. This is undoubtedly a challenging task, but ignoring these complexities can result in misleading assessments of the impacts of climate change, suboptimal management outcomes, and the inefficient allocation of resources for climate adaptation.     

Visitor management and ecological integrity: one example of an integrated management approach using Decision Analysis

In this paper we argue in favour of using a decision analysis framework for more integrated decision-making when managing protected areas. Such an approach will enable agencies to balance between the frequently conflicting goals of visitor management and ecological integrity. We present a case study from the West Coast Trail in Pacific Rim National Park Reserve, BC, Canada, in which we use a hybrid of ELECTRE and AHP to establish a ranking of several management options. We conclude by suggesting that such a more formal framework constitutes a more objective decision support tool, assists in framing relevant management questions and tradeoffs, and at the same time provides guidance for data collection.     

Hierarchical Activity Model for Risk‐Based Decision Making

For the practical implementation of the assessment of environmental impact, actual procedures and data requirements should be clarified so that industrial decision makers understand them. Researchers should consider local risks related to processes and environmental impact throughout the life cycle of products simultaneously to supervise these adverse effects appropriately. Life cycle assessment (LCA) is a useful tool for quantifying the potential impact associated with a product life cycle. Risk assessment (RA) is a widely used tool for identifying chemical risks in a specific situation. In this study, we integrate LCA and RA for risk‐based decision making by devising a hierarchical activity model using the type‐zero method of integrated definition language (IDEF0). The IDEF0 activity modeling language has been applied to connect activities with information flows. Process generation, evaluation, and decision making are logically defined and visualized in the activity model with the required information. The activities, information flows, and their acquisitions are revealed, with a focus on which data should be collected by on‐site engineers. A case study is conducted on designing a metal cleaning process reducing chemical risks due to the use of a cleansing agent. LCA and RA are executed and applied effectively on the basis of integrated objective settings and interpretation. The proposed activity model can be used as a foundation to incorporate such assessments into actual business models.     

Management of ecosystem services in mountain forests: Review of indicators and value functions for model based multi-criteria decision analysis

The main ecosystem services (ES) central European mountain forests provide are: protection against gravitational hazards, timber production, recreation, biodiversity conservation and carbon storage, which are all in high demand. These demands make managing mountain forests a challenging task, involving manifold synergies and conflicts between the different ES. There is therefore an urgent need for appropriate concepts and tools for support decisions in forest management and planning (FMP) to take into consideration all ES and to manage the wide variety of information types, parameters and uncertainties involved in assessing the sustainability of ES. Multi-criteria decision analysis (MCDA) provides a suitable set of methods for sustainability evaluations. In this study sustainability means the persistent fulfilment of the required ES. To address all the phases of the FMP process, MCDA and forest models should be applied together, with indicators providing the main interfaces to combine them. This paper aims to: i) review assessment approaches in order to select appropriate and widely accepted indicators for measuring and assessing the effects of different silvicultural management alternatives on forest ES, and ii) present additional standardisation approaches (value functions) for each indicator. Standardisations are necessary to make the different ES comparable and to study synergies and trade-offs between different management objectives in MCDA. The main ES in central European mountain regions are considered, with a clear focus on those indicators that are directly derivable from forest model outputs and that can refer to sustainable forest management practices. The scales considered are that of the single forest stand and of the larger forest management unit. A holistic indicator-based analysis framework for FMP in mountain forests can be built using the indicators and value functions described. The influence of different management alternatives on ES can then be evaluated, taking into consideration the instruments and information on forest management (forest models, inventory) available. All indicators are selected according to existing and approved approaches that only require data that is normally available in operational forest management. The framework can thus be an important element in developing a decision support system for FMP in mountain forests.