Combining Population Viability Analysis with Decision Analysis

Management of endangered species requires methods to assess the effects of strategies, providing a basis for deciding on a best course of action. An important component of assessment is population viability analysis (PVA). The latter may be formally implemented through decision analysis (DA). These methods are most useful for conservation when used in conjunction. In this paper we outline the objectives and the potential of both frameworks and their overlaps. Both are particularly helpful when dealing with uncertainty. A major problem for conservation decision-making is the interpretation of observations and scientific measurements. This paper considers probabilistic and non-probabilistic approaches to assessment and decision-making and recommends appropriate contexts for alternative approaches.     

Ecological land suitability analysis through spatial indicators: An application of the Analytic Network Process technique and Ordered Weighted Average approach

Multicriteria-Spatial Decision Support Systems (MC-SDSS) are increasingly popular tools in decision-making processes and in policy making, thanks to their significant new capabilities in the use of spatial or geospatial information.Many spatial problems are complex and require the use of integrated analysis and models. The present paper illustrates the development of a MC-SDSS approach for studying the ecological connectivity of the Piedmont Region in Italy. The MC-SDSS model considers ecological and environmental spatial indicators which are combined by integrating the Multicriteria Decision Aiding (MCDA) technique named Analytic Network Process (ANP) and the Ordered Weighted Average (OWA) approach. The ANP is used for the elicitation of attribute weights while the OWA operator function is used to generate a wide range of decision alternatives for addressing uncertainty associated with interaction between multiple criteria. The usefulness of the approach is illustrated by different OWA scenarios that report the ecological connectivity index on a scale between 0 and 1. The OWA scenarios are intended to quantify the level of risk taking (i.e., optimistic, pessimistic, and neutral) and to facilitate a better understanding of patterns that emerge from decision alternatives involved in the decision-making process.The purpose of the research is to generate a final map representing the ecological connectivity index of each area in the region under analysis, to be used as a decision variable in spatial planning. In particular, by using the resulting index map as a means of analysis, it is possible to identify, for the sake of nature conservation, some critical areas needing mitigation measures. In addition, areas with high ecological connectivity values can be identified and monitoring procedures can therefore be planned. The study concludes highlighting that the applied methodology is an effective tool in providing decision support for spatial planning and sustainability assessments.     

Stochastic multi-attribute analysis (SMAA) as an interpretation method for comparative life-cycle assessment (LCA)

Purpose

Comparative life-cycle assessments (LCAs) today lack robust methods of interpretation that help decision makers understand and identify tradeoffs in the selection process. Truncating the analysis at characterization is misleading and existing practices for normalization and weighting may unwittingly oversimplify important aspects of a comparison. This paper introduces a novel approach based on a multi-criteria decision analytic method known as stochastic multi-attribute analysis for life-cycle impact assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces.

Methods

To contrast different valuation methods, this study performs a comparative LCA of liquid and powder laundry detergents using three approaches to normalization and weighting: (1) characterization with internal normalization and equal weighting, (2) typical valuation consisting of external normalization and weights, and (3) SMAA-LCIA using outranking normalization and stochastic weighting. Characterized results are often represented by LCA software with respect to their relative impacts normalized to 100 %. Typical valuation approaches rely on normalization references, single value weights, and utilizes discrete numbers throughout the calculation process to generate single scores. Alternatively, SMAA-LCIA is capable of exploring high uncertainty in the input parameters, normalizes internally by pair-wise comparisons (outranking) and allows for the stochastic exploration of weights. SMAA-LCIA yields probabilistic, rather than discrete comparisons that reflect uncertainty in the relative performance of alternatives.

Results and discussion

All methods favored liquid over powder detergent. However, each method results in different conclusions regarding the environmental tradeoffs. Graphical outputs at characterization of comparative assessments portray results in a way that is insensitive to magnitude and thus can be easily misinterpreted. Typical valuation generates results that are oversimplified and unintentionally biased towards a few impact categories due to the use of normalization references. Alternatively, SMAA-LCIA avoids the bias introduced by external normalization references, includes uncertainty in the performance of alternatives and weights, and focuses the analysis on identifying the mutual differences most important to the eventual rank ordering.

Conclusions

SMAA-LCIA is particularly appropriate for comparative LCAs because it evaluates mutual differences and weights stochastically. This allows for tradeoff identification and the ability to sample multiple perspectives simultaneously. SMAA-LCIA is a robust tool that can improve understanding of comparative LCA by decision or policy makers.     

Decision‐making under climatic uncertainty: A case study involving an Australian Ramsar‐listed wetland

Summary Conservation management in agricultural landscapes involves identification and prioritization of assets, and interventions to reverse or arrest decline. Planning requires synthesis of hydrological, ecological and agronomic information and intuitions. We provide a case study involving the Lake Warden Wetland System, a Ramsar‐listed site on the south coast of Western Australia threatened by salinity and flooding. As the relative merits of management options (including engineering‐based solutions and catchment revegetation) may be sensitive to climate change, we captured our knowledge and understanding of the effectiveness of options under different climate change scenarios using Bayesian belief networks. We insulated against overconfidence by an info‐gap analysis that describes the trade‐off between aspiration and immunity to uncertainty. Only engineering‐based solutions offer reasonable prospects for achieving stated conservation goals in the Lake Warden Wetland System within a 25‐year time horizon. Marginal gains derived from co‐investment in revegetation varied among the assets. We advocate explicit treatment of uncertainty and risk‐based approaches to decision‐making to equip managers with a means of progressing conservation goals. The complementary insights offered by Bayesian belief networks and info‐gap analysis provide a sound basis for managers to assess the extent to which candidate management actions are robust to uncertainty.     

Incorporating uncertainty and social values in managing invasive alien species: a deliberative multi-criteria evaluation approach

The management of Invasive Alien Species (IAS) is stymied by complex social values and severe levels of uncertainty. However, these two challenges are often hidden in the conventional model of management by “value-free” analyses and probability-based estimates of risk. As a result, diverse social values and wide margins of error in risk assessment carry zero weights in the decision-making process, leaving IAS risk decisions to be made in the wake of political pressure and the crisis atmosphere of incursion. We propose to use a Deliberative Multi-Criteria Evaluation (DMCE) to incorporate multiple social values and profound uncertainty into decision-making processes. The DMCE process combines the advantages of conventional multi-criteria decision analysis methods with the benefits of stakeholder participation to provide an analytical structure to assess complex multi-dimensional objectives. It, therefore, offers an opportunity for diverse views to enter the decision-making process, and for the negotiation of consensus positions. The DMCE process can also function as a platform for risk communication in which scientists, stakeholders, and decision-makers can interact and discuss the uncertainty associated with biological invasions. We examine two case studies that demonstrate how DMCE provides scientific rigor and transparency in the decision-making process of invasion risk management. The first case regards pre-border priority ranking for potential invasive species and the second relates to selecting the most desirable policy option for managing a post-border invader.     

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.     

Spatial Uncertainty and Ecological Models

Applied ecological models that are used to understand and manage natural systems often rely on spatial data as input. Spatial uncertainty in these data can propagate into model predictions. Uncertainty analysis, sensitivity analysis, error analysis, error budget analysis, spatial decision analysis, and hypothesis testing using neutral models are all techniques designed to explore the relationship between variation in model inputs and variation in model predictions. Although similar methods can be used to answer them, these approaches address different questions. These approaches differ in (a) whether the focus is forward or backward (forward to evaluate the magnitude of variation in model predictions propagated or backward to rank input parameters by their influence); (b) whether the question involves model robustness to large variations in spatial pattern or to small deviations from a reference map; and (c) whether processes that generate input uncertainty (for example, cartographic error) are of interest. In this commentary, we propose a taxonomy of approaches, all of which clarify the relationship between spatial uncertainty and the predictions of ecological models. We describe existing techniques and indicate a few areas where research is needed.     

Comprehensive criteria for biodiversity evaluation in conservation planning

In this paper we present the results of a multi-criteria decision analysis used to identify a comprehensive set of criteria for assigning biodiversity value to sites for conservation planning. For effective conservation management, biodiversity value needs to be a composite of biotic and abiotic factors. However, in the reserve design literature, conservation value is assigned with a limited set of metrics usually based on comprehensiveness, representativeness and persistence which may be insufficient at fully capturing biodiversity value. A group of conservation specialists in California, USA, used a multi-criteria decision making framework to elucidate and weight criteria for scoring biodiversity value at sites. A formal model for consensus and negotiation was applied to aggregate individuals’ criteria weights across all group members. The group identified ecological condition, followed by biotic composition as the most important contributors to site conservation value. Long- and short-term threats causing fragmentation and degradation are also important criteria to consider. Key criteria are identified for which further data collection would serve the greatest purpose in prioritizing sites and the role of prioritization criteria in the larger context of systematic conservation planning is discussed. With the recognition that biodiversity value plays an important role in conservation decisions, the criteria presented here represents a comprehensive suite of factors to consider when assigning biodiversity value to sites for conservation planning. These can serve as an encompassing list which other groups can customize for the purpose of biodiversity evaluation for alternative conservation planning contexts.     

Creating Larger and Better Connected Protected Areas Enhances the Persistence of Big Game Species in the Maputaland-Pondoland-Albany Biodiversity Hotspot

The ideal conservation planning approach would enable decision-makers to use population viability analysis to assess the effects of management strategies and threats on all species at the landscape level. However, the lack of high-quality data derived from long-term studies, and uncertainty in model parameters and/or structure, often limit the use of population models to only a few species of conservation concern. We used spatially explicit metapopulation models in conjunction with multi-criteria decision analysis to assess how species-specific threats and management interventions would affect the persistence of African wild dog, black rhino, cheetah, elephant, leopard and lion, under six reserve scenarios, thereby providing the basis for deciding on a best course of conservation action in the South African province of KwaZulu-Natal, which forms the central component of the Maputaland-Pondoland-Albany biodiversity hotspot. Overall, the results suggest that current strategies of managing populations within individual, small, fenced reserves are unlikely to enhance metapopulation persistence should catastrophic events affect populations in the future. Creating larger and better-connected protected areas would ensure that threats can be better mitigated in the future for both African wild dog and leopard, which can disperse naturally, and black rhino, cheetah, elephant, and lion, which are constrained by electric fences but can be managed using translocation. The importance of both size and connectivity should inform endangered megafauna conservation and management, especially in the context of restoration efforts in increasingly human-dominated landscapes.     

生态效益评价内容和评价指标筛选

生态效益评价的重要性已经广为人知,但评价内容和指标不统一,大大影响了评价结果的可信度。基本上是各说各有理,很难相互比较。在分析国内外相关生态系统评价方法基础上,提出生态效益评价框架,强调生态效益与经济效益和社会效益共同构成了人类社会的价值判断标准和决策依据,是经济效益和社会效益的基础,应该包括生态系统整体贡献,考虑与生态系统的动态变化的关系;并探讨了生态效益评价指标筛选的原则(关联性、灵敏性、层级性、决策导向性、代表性、可行性、独立性及经济适用性和社会可接受性);提出了生态效益指标筛选的多准则综合法和生态效益评价指标检验的3个标准(可定量化、专一化和震撼性)。本研究将为生态效益指标体系的构建提供重要参考。     

Uncertainty in Impact and Externality Assessments - Implications for Decision-Making (13 pp)

Goal, Scope and Background Many disciplines, amongst them LCIA, environmental impact and external cost assessments, are often faced with evaluating trade-offs between two or more alternative options in terms of a range of incommensurable indicators. Using process modeling and valuation, these indicators are quantified at mid- or endpoint levels. Recent discussion amongst LCA experts showed that because of the mutually exclusive aspects of uncertainty and relevance, the midpoint/endpoint debate is controversial and difficult to reconcile. This article is aimed at a more quantitative analysis of mid- and endpoint impacts, and the implications of uncertainty for decision-making. Methods The consequences for decision-making of uncertainties of endpoints are analysed quantitatively for the example of ExternE results, by employing statistical hypothesis testing. The Analytic Hierarchy Process (AHP) is then used to demonstrate the use of multi-criteria techniques at midpoint levels. Results and Discussion Statistical hypothesis testing at the endpoint level shows that for the ExternE example, probabilities of mistakenly favouring one alternative over another when they are in reality indistinguishable can be as high as 80%. Therefore, the best estimate of external cost is inadequate for most policy making purposes. Indicators at midpoint levels are more certain, but since they are only \proxy attributes\, they carry a hidden uncertainty in their relevance. Conclusion If endpoint information is too uncertain to allow a decision to be made with reasonable confidence, then the assessment can be carried out in midpoint terms. However, midpoint indicators are generally further removed from people's experience, and less relevant to the question that people actually want to solve. Nevertheless, if this ultimate question is unanswerable (within the certainty required by the decision-maker), a decision can be made on the basis of stakeholders' subjective judgments about the more certain midpoint levels. The crucial point is that these judgments are able to intuitively incorporate many aspects that impact modeling and valuation has trouble quantifying, such as perceived risk, distribution of burdens and benefits, equity, ethical, moral, religious and political beliefs and principles, immediacy and reversibility of potential impacts, voluntariness, controllability and familiarity of exposure, or perceived incompleteness of human knowledge.     

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.     

基于地理模型与优化的城市扩张与生态保护二元空间协调优化

协调城市扩张与生态敏感区保护之间的矛盾是当前我国新型城镇化建设中的一项重要任务,但基于传统供需平衡模式或历史惯性驱动模拟的城市规划布局可能导致一系列潜在的生态环境问题。根据城市发展具有历史惯性驱动和空间规划引导的双重特性,提出将地理模拟与优化(Geographical Simulation and Optimization Systems,简写为GeoSOS)等复杂GIS空间分析技术引入规划决策分析。通过利用最小累积阻力模型获取生态敏感区保护压力格局,并利用元胞自动机模型进行城市扩张模拟,分析城市惯性扩张模式对生态敏感区的潜在影响;然后根据生态敏感区保护和城市空间扩张的协调性发展目标进行生态适宜性评价,进而利用蚁群智能空间优化配置模型产生一种优化的城市空间布局方案。研究以我国珠江三角洲地区的广州市为案例,详细分析了基于GeoSOS的城市扩张与生态保护的协调决策过程。结果表明,整合了城市发展惯性与生态敏感区保护双重目标的空间优化布局方案,比单纯基于地理模拟进行规划布局更符合生态型城市建设需求,研究所提出的城市与生态二元空间协调分析框架可为城市规划提供可靠的定量决策支撑。     

Foraging theory provides a useful framework for livestock predation management

A societal shift toward plant dominant diets and a reduction in livestock rearing could have broad social, environmental and conservation benefits. Livestock husbandry, however, has a wealthy cultural history, strong support and high consumer demand. It is therefore likely to continue as a major land use and conservation issue for predators. From a producer’s perspective, the primary goals of livestock protection are maximising, or at least maintaining, production by minimising losses and mitigating detriment to stock welfare. Lethal removal of predators remains a commonplace solution. Such management measures are questionable as they raise animal welfare and conservation concerns, risk inhibiting ecological processes, are often expensive, and in some circumstances, exacerbate livestock predation problems. Non-lethal alternatives can facilitate co-existence between livestock farmers and predators, ideally reducing the ecological impact of pastoralism and achieving conservation goals. The need for rigorous study of non-lethal approaches has however been recently highlighted. Tools and methods involved in livestock protection, as well as the theoretical basis of how we perceive and manage the problem, require deeper consideration. Non-lethal approaches require knowledgeable implementation and an effective decision making system is a prerequisite for successful practice. Livestock predation and its prevention are fundamentally influenced by the underlying principles of foraging ecology and risk theory. We propose that manipulating elements of Brown’s (1988) quitting harvest rate model provides a useful conceptual framework for reducing livestock predation and encouraging coexistence.     

生态资产核算与生态系统服务评估:概念交汇与重点方向

面向“山水林田湖草”统一管理的现实目标,对生态资产的准确刻画加深了资源管理者和使用者对生态系统服务的认识,是生态系统服务理论从学术研讨向决策实践过渡的重要桥梁。然而,当前的生态资产核算结果仍存在着较大的不确定性,使其决策支持作用受到质疑。基于对生态资产研究近今进展的总结,生态资产实际核算一般取自然资本与生态系统服务的交集分别作为存量和流量。如若将生态资产作为干部离任审计依据,则须把握先实物量后价值量的原则。在当前国际研究中,生态资产已经成为区域景观管理和农户生计决策的重要绩效评估与情景优选工具。完善生态系统服务评估模型、明晰生态系统服务供需关系、规范生态资产价值核算方法、提升生态资产决策支持能力4项内容应引起未来生态资产研究的重点关注。     

The Use of Probabilistic Analysis to Improve Decision-Making in Environmental Regulation in a Developing Context: The Case of Arsenic Regulation in Chile

Using probabilistic analysis may be very useful for risk management in developing countries, where information, resources, and technical expertise are often scarce. Currently, most regulatory agencies recommend using deterministic approaches for the analysis of problems relating to decision-making. However, this approach does not incorporate uncertainty in the variables, nor the propagation of uncertainty through the different processes in which they are involved. The complexity of the problem is therefore arbitrarily reduced, and valuable information that could be useful for proposing realistic policies is not considered. This article compares the results of a deterministic analysis with those of a probabilistic one for regulating arsenic in Chile, and differences are established for public policy as a result of building uncertainty into the analysis. It is concluded that the use of a deterministic approach can lead to higher risks than necessary and that probabilistic results can help the regulator negotiate stricter standards. Alternatively, the regulator may end up imposing much higher costs to sources than originally expected as these will be forced to use expensive technology to comply consistently with a given standard.     

Introducing weights to life cycle sustainability assessment—how do decision-makers weight sustainability dimensions?

Purpose

Decisions based on life cycle sustainability assessment (LCSA) pose a multi-criteria decision issue, as impacts on the three different sustainability dimensions have to be considered which themselves are often measured through several indicators. To support decision-making at companies, a method to interpret multi-criteria assessment and emerging trade-offs would be beneficial. This research aims at enabling decision-making within LCSA by introducing weights to the sustainability dimensions.

Methods

To derive weights, 54 decision-makers of different functions at a German automotive company were asked via limit conjoint analysis how they ranked the economic, environmental, and social performance of a vehicle component. Results were evaluated for the entire sample and by functional clusters. Additionally, sustainability respondents, i.e., respondents that dealt with sustainability in their daily business, were contrasted with non-sustainability respondents. As a last step, the impact of outliers was determined. From this analysis, practical implications for ensuring company-optimal decision-making in regard to product sustainability were derived.

Results and discussion

The results showed a large spread in weighting without clear clustering. On average, all sustainability dimensions were considered almost equally important: the economic dimension tallied at 33.5%, the environmental at 35.2%, and the social at 31.2%. Results were robust as adjusting for outliers changed weights on average by less than 10%. Results by function showed low consistency within clusters hinting that weighting was more of a personal than a functional issue. Sustainability respondents weighted the social before the environmental and economic dimension while non-sustainability respondents put the economic before the other two dimensions. Provided that the results of this research could be generalized, the retrieved weighting set was seen as a good way to introduce weights into an operationalized LCSA framework as it represented the quantification of the already existing decision process. Therefore, the acceptance of this weighting set within the respective company was expected to be increased.

Conclusions

It could be shown that conjoint analysis enabled decision-making within LCSA by introducing weights to solve a multi-criteria decision issue. Furthermore, implications for practitioners could be derived to ensure company-optimal decision-making related to product sustainability. Future research should look at expanding the sample size and geographical scope as well as investigating the weighting of indicators within sustainability dimensions and the drivers that influence personal decision-making in regard to weighting sustainability dimensions.

     

Best of both worlds: Combining ecological and social research to inform conservation decisions in a Neotropical biodiversity hotspot

Conservation decision is a challenging and risky task when it aims at prioritizing species or protected areas (PAs) to prevent extinction while ensuring fair treatment of all stakeholders. Better conservation decisions are those made upon a broader evidence base that includes both ecological and social considerations. However, in some of the most biodiverse ecosystems on Earth — tropical forests, for instance — multicriteria decision-making has been constrained by the following (i) ecological and social datasets available have been obtained in an independent, non-integrated manner, with social data typically more scarce than ecological ones, and (ii) capacity in social and/or interdisciplinary data analysis among decision-maker is limited. We describe a conservation prioritization exercise that combined findings from independent ecological and social research conducted in the Brazilian Atlantic Forest, and propose methods to integrate, analyze and visualize data. We found that the outcomes based on combined ecological and social research findings were, in some cases, different from those based on any of these lines of evidence alone. Indeed, the input from relatively basic social research significantly changed the outcomes of decision-making based on the results of ecological research. Results corroborate the importance and cost-effectiveness of broadening the interdisciplinary evidence base for conservation decision-making, even when social data is scarce and analytical capacity is limited.     

Using and communicating uncertainty for the effective control of invasive non-native species

Estimates of quantities needed to plan invasive species control, such as population size, are always uncertain; this is an issue that can become a problem when mishandled in ecological science and its communication. The complexities of incorporating uncertainty into sophisticated decision-support tools may be a barrier to their use by decision makers, leading to decisions being made without due regard to uncertainty and risking misplaced certainty of predicted outcomes. We summarise ways in which uncertainty has been incorporated into and used to advise decisions on the management of invasive non-native species and other problem species, and offer a simple conceptual model for accommodating and using uncertainty at the planning stage. We also demonstrate how frequently uncertainty has been misused and miscommunicated in the wildlife management literature. We contend that uncertainty in estimates of natural quantities must be acknowledged, can inform decisions and can be made to derive decisions, and should not be ignored if invasive species policy is to be delivered effectively. Uncertainty must be communicated thoroughly and correctly by scientists if decision makers are to understand its consequences for planning and resourcing control programmes.     

The comparative method in conservation biology

The phylogenetic comparative approach is a statistical method for analyzing correlations between traits across species. Whilst it has revolutionized evolutionary biology, can it work for conservation biology? Although it is correlative, advocates of the comparative method hope that it will reveal general mechanisms in conservation, provide shortcuts for prioritizing conservation research, and enable us to predict which species will experience (or create) problems in the future. Here, we ask whether these stated management goals are being achieved. We conclude that comparative methods are stimulating research into the ecological mechanisms underlying conservation, and are providing information for preemptive screening of problem species. But comparative analyses of extinction risk to date have tended to be too broad in scope to provide shortcuts to conserving particular endangered species. Correlates of vulnerability to conservation problems are often taxon, region and threat specific, so models must be narrowly focused to be of maximum practical use.