Model-based Conservation Decision Aiding in the Presence of Goal Conflicts and Uncertainty

Goal conflicts and uncertainty are two major problems in decision-making for conservation and species protection. Conflicts can often be found between ecological goals on the one hand and socio-economic goals on the other, but also among different ecological goals. They can be formally analysed by methods of multi-criteria analysis. As the solution of a multi-criteria decision problem usually depends on the weights put on the individual criteria (objectives), sensitivity and robustness analyses are necessary to understand the decision problem, concentrate on the essential aspects, and support actual decision processes fully. Uncertainty in the decision problem is often caused by scarcity of information needed to predict the consequences of management actions. The so-called outranking concept proved very useful in the consideration of such uncertainty. Based on a simple fictitious case study the paper demonstrates how multi-criteria decision analysis (in particular the PROMETHEE outranking method) in combination with population model analysis can assist in conservation biological decision-making.     

A Comparison of Predictive Methods in Extinction Risk Studies: Contrasts and Decision Trees

Over the last two decades an increasing emphasis has been placed on the importance of controlling for phylogeny when examining cross-species data; so-called comparative methods. These methods are appropriate for testing hypotheses about correlations between evolutionary events in the history of a clade and adaptive responses to those changes. When this approach is applied to extinction risk, possible correlations between evolutionary changes in, for example, body size or habitat specialisation and some measure(s) of current threat status are examined. However, there may be a mismatch here between the results of such studies, and the real, pragmatic needs of species conservation. This kind of approach certainly adds to our knowledge of some fundamental processes, but it is more difficult to see how this can be applied to conservation decision-making. For more practical purposes a decision-tree approach can be extremely useful. This paper illustrates the use of a contrasts based analysis of extinction risk compared with a decision-tree analysis for Galliformes (Aves). While the contrasts analyses concur with some general macroecological trends found in other studies, the decision-tree models provide lists of species predicted to be more at risk than current assessments would suggest. We argue that in practical terms, decision tree models might be more useful than a macroecological linear model-based approach.     

Risk-Based Approaches to Managing Contaminants in Catchments

Risk-based methods promise improved decision-making for managing of contaminants, such as salinity, sediments, nutrients, and toxicants, that can adversely affect the ecological condition of aquatic ecosystems. Two aspects of ecological risk assessment (ERA) and management—stakeholder involvement and more quantitative approaches to risk analysis—are particularly challenging. Stakeholder involvement is crucial both in the risk assessment process and the development, acceptance, and implementation of a risk management plan. Additionally, a number of quantitative approaches (particularly Bayesian approaches and multi-criteria decision-making) have been identified as having the potential to include expert-based inputs into risk-based decision-making. These offer promise for better inclusion of stakeholder knowledge and preferences into the decision-making process, and for improving the links between stakeholder inputs and potential risks to the ecological condition of the system. A major challenge for ecologists and natural resource managers is to make the ERA process more quantitative. Most ERAs conducted to date have been qualitative assessments that suffer from a number of deficiencies, the most serious being the lack of transparency and a reliance on subjective judgments. This article argues that the most productive way forward may be to use Bayesian methods to couple existing process-based models, empirical relationships based on good data, and expert opinion, to make the analysis of ecological risks more robust, consistent, and repeatable.     

Beyond F<Subscript>ST</Subscript>: Analysis of population genetic data for conservation

Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structure, quantification of gene flow, and inference of demographic history. We cover both allele-frequency and sequence-based approaches, with a special focus on methods relevant to conservation genetic applications. Although classical population genetic approaches such as F _st (and its derivatives) have carried the field thus far, newer, more powerful, methods can infer much more from the data, rely on fewer assumptions, and are appropriate for conservation genetic management when precise estimates are needed.     

基于GIS的山西省矿产资源规划环境影响评价

基于目前我国矿产资源规划编制过程中的环境评价,提出了一种基于GIS(Geographic Information System)技术,实现矿产资源规划环境影响定量评价方法。在对规划区环境现状进行大尺度综合评价的基础上,利用GIS的空间分析功能,有针对性的实现规划区敏感环境保护目标的筛选、环境影响范围的确定和矿产资源开发对环境保护目标影响程度的定量表达。结合山西省矿产资源规划方案,根据山西省各区域资源开发对敏感保护目标的影响特征,在定量计算各区域环境现状综合评价分级的基础上,分别对其生态环境、水环境、地质环境等方面的影响进行评价和预测,结果显示:山西省矿产资源规划空间布局总体合理,不会对敏感环境保护目标造成严重破坏,但部分矿产资源开采区距离保护目标较近,需要在矿产资源规划实施中加以保护。提出的评价方法较好地兼顾矿产资源开发与区域环境保护,可以有效地提高矿产资源规划环境评价结果的准确性和科学性;该方法可以综合地、定量地评价矿产资源规划对不同环境保护目标的影响程度和范围,为矿产资源规划方案的修订提供有价值的参考。     

The expansion of conservation genetics

The 'crisis discipline' of conservation biology has voraciously incorporated many technologies to speed up and increase the accuracy of conservation decision-making. Genetic approaches to characterizing endangered species or areas that contain endangered species are prime examples of this. Technical advances in areas such as high-throughput sequencing, microsatellite analysis and non-invasive DNA sampling have led to a much-expanded role for genetics in conservation. Such expansion will allow for more precise conservation decisions to be made and, more importantly, will allow conservation genetics to contribute to area- and landscape-based decision-making processes.     

Uncertainty Analysis and Toxicity Classification in Life-Cycle Assessment Using the Case-study of Gas Purification Systems

This comprehensive thesis structures the decision-making process for making a choice of the most adequate gas purification system (GasPS). Various gas purification technologies (biofilter, activated carbon filter, catalytic oxidation, thermo-reactor) have been evaluated based on an industrial case-study for waste gas streams. The ecological performance was quantified using the life-cycle impact assessment methods Eco-Indicator 95 and Swiss Ecopoints (environmental scarcities). Both life-cycle impact assessment methods have been improved by a new classification method for volatile organic compounds (VOCs) which considers the environmental fate and exposure as well as the toxicity of these compounds. For life-cycle assessment, a detailed quantitative uncertainty analysis was carried out using Monte Carlo simulation. Based on the uncertainty analysis, developing statements about the significance of the results and of relative differences between various GasPS alternatives has been possible. The eco-efficiency of the investigated GasPSs was finally characterised based on four indicators: Net Ecological Benefit (NEB_N), Ecological Yield Efficiency (lgEYE), Net Present Value (NPV), and Ecological-Economic Efficiency (EEE).     

Approaches to Ecological Risk Characterization and Management: Selecting the Right Tools for the Job

Chemical-specific hazard quotient (HQ) risk characterization in ecological risk assessment (ERA) can be a value-added tool for risk management decision-making at chemical release sites, when applied appropriately. However, there is little consensus regarding how HQ results can be used for risk management decision-making at the population, community, and ecosystem levels. Furthermore, stakeholders are reluctant to consider alternatives to HQ results for risk management decisions. Chemical-specific HQs risk characterization should be viewed as only one of several approaches (i.e., tools) for addressing ecological issues; and in many situations, other quantitative and qualitative approaches will likely result in superior risk management decisions. The purpose of this paper is to address fundamental issues and limitations associated with chemical-specific HQ risk characterization in ERA, to identify when it may be appropriate, to explore alternatives that are currently available, and to identify areas that could be developed for the future. Several alternatives (i.e., compensatory restoration, performance-based ecological monitoring, ecological significance criteria, net environmental benefit analysis), including their limitations, that can supplement, augment, or substitute for HQs in ERA are presented. In addition, areas of research (i.e., wildlife habitat assessment/landscape ecology/population biology, and field validated risk-based screening levels) that could yield new tools are discussed.     

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.     

Evaluation of Sampling-Based Methods for Sensitivity Analysis: Case Study for the E. coli Food Safety Process Risk Model

This article evaluates selected sensitivity analysis methods applicable to risk assessment models with two-dimensional probabilistic frameworks, using a microbial food safety process risk model as a test-bed. Six sampling-based sensitivity analysis methods were evaluated including Pearson and Spearman correlation, sample and rank linear regression, and sample and rank stepwise regression. In a two-dimensional risk model, the identification of key controllable inputs that can be priorities for risk management can be confounded by uncertainty. However, despite uncertainty, results show that key inputs can be distinguished from those that are unimportant, and inputs can be grouped into categories of similar levels of importance. All selected methods are capable of identifying unimportant inputs, which is helpful in that efforts to collect data to improve the assessment or to focus risk management strategies can be prioritized elsewhere. Rank-based methods provided more robust insights with respect to the key sources of variability in that they produced narrower ranges of uncertainty for sensitivity results and more clear distinctions when comparing the importance of inputs or groups of inputs. Regression-based methods have advantages over correlation approaches because they can be configured to provide insight regarding interactions and nonlinearities in the model.     

Landscape evaluation for ecosystem planning

Landscape evaluation is important in the conservation of biodiversity and sustainable development. The objective of this paper is to review and explore methods for evaluation of landscapes for ecosystem planning. Ecosystem planning is the process of land use decision-making that considers organisms and processes that characterize the ecosystem as a whole. Risk assessments, precautionary principles, adaptive management and scenario approaches are adopted to cope with the uncertainty of nature, which is an obstacle in ecosystem planning. Special attention is needed in the analysis of status and troubleshooting in the planning scheme, which is a selection of the appropriate approach and model to find problems in the present situation. There are two approaches to set targets in ecosystem planning, the species approach and the ecosystem approach. The species approach aims to protect particular species, and the ecosystem approach aims to protect total ecosystems including the species. In Europe, ecotope or biotope mapping has been developed in ecosystem planning. An ecotope is often identified by vegetation that represents a group of wildlife, but many species require combinations of different ecotopes. Landscapes have come to be recognized as a unit for ecosystem planning. Potential assessment is a method to estimate a potential of a local space or a landscape to realize an ecosystem or species habitat, and this method has been used in HEP and GAP analysis in the USA and Ecological Networks in Europe. Some examples of ecosystem planning of national and regional scales in Japan are introduced.     

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.     

Incorporating multiple criteria into the design of conservation area networks: a minireview with recommendations

We provide a review of multicriteria decision-making (MCDM) methods that may potentially be used during systematic conservation planning for the design of conservation area networks (CANs). We review 26 methods and present the core ideas of 19 of them. We suggest that the computation of the non-dominated set (NDS) be the first stage of any such analysis. This process requires only that alternatives be qualitatively ordered by each criterion. If the criteria can also be similarly ordered, at the next stage, Regime is the most appropriate method to refine the NDS. If the alternatives can also be given quantitative values by the criteria, Goal Programming will prove useful in many contexts. If both the alternatives and the criteria can be quantitatively evaluated, and the criteria are independent of each other but may be compounded, then multi-attribute value theory (MAVT) should be used (with preferences conveniently elicited by a modified Analytic Hierarchy Process (mAHP) provided that the number of criteria is not large).     

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.     

A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance

Summary .  We consider the analysis of clinical trials that involve randomization to an active treatment ( T  = 1) or a control treatment ( T  = 0), when the active treatment is subject to all-or-nothing compliance. We compare three approaches to estimating treatment efficacy in this situation: as-treated analysis, per-protocol analysis, and instrumental variable (IV) estimation, where the treatment effect is estimated using the randomization indicator as an IV. Both model- and method-of-moment based IV estimators are considered. The assumptions underlying these estimators are assessed, standard errors and mean squared errors of the estimates are compared, and design implications of the three methods are examined. Extensions of the methods to include observed covariates are then discussed, emphasizing the role of compliance propensity methods and the contrasting role of covariates in these extensions. Methods are illustrated on data from the Women Take Pride study, an assessment of behavioral treatments for women with heart disease.     

Surveillance for threatened and invasive species when uncertainty is severe

Aim This study develops methods for efficient surveillance and monitoring systems to address a wide range of problems in biosecurity, ecology and conservation biology. It focuses especially on surveillance systems relevant for management that aims to reduce trade in threatened species and curb the spread of potential pests and diseases. Location Melbourne, Australia. Methods This paper develops different approaches to make decisions about the allocation of resources that aim to avoid unacceptable outcomes. The analysis solves for the optimal allocation of surveillance effort in each of two facilities as a function of the arrival rates of invasive species in two facilities (that is, when the arrival rates are known). However, when arrival rates are unknown, it is not possible to solve for this optimum. The analysis also provides a satisficing approach for situations in which arrival rates are unknown, in which a degree of tolerance for deviating from the optimal solution is specified. Results The study provides simple analytical solutions to these two problems, analogous to results developed earlier in operations research. The analysis is illustrated with an example of the inspection of quarantine facilities for pests and diseases associated with trade. Main conclusions The best surveillance strategy depends on the choice of an objective function and the attitude of the decision‐maker to the robustness of the decision. The example application could be adapted to many other environmental surveillance scenarios.     

How to value biodiversity in environmental management?

Biodiversity is globally recognised as a cornerstone of healthy ecosystems, and biodiversity conservation is increasingly becoming one of the important aims of environmental management. Evaluating the trade-offs of alternative management strategies requires quantitative estimates of the costs and benefits of their outcomes, including the value of biodiversity lost or preserved. This paper takes a decision-analytic standpoint, and reviews and discusses the alternative aspects of biodiversity valuation by dividing them into three categories: socio-cultural, economic, and ecological indicator approaches. We discuss the interplay between these three perspectives and suggest integrating them into an ecosystem-based management (EBM) framework, which permits us to acknowledge ecological systems as a rich mixture of interactive elements along with their social and economic aspects. In this holistic framework, socio-cultural preferences can serve as a tool to identify the ecosystem services most relevant to society, whereas monetary valuation offers more globally comparative and understandable values. Biodiversity indicators provide clear quantitative measures and information about the role of biodiversity in the functioning and health of ecosystems. In the multi-objective EBM approach proposed in the paper, biodiversity indicators serve to define threshold values (i.e., the minimum level required to maintain a healthy environment). An appropriate set of decision-making criteria and the best method for conducting the decision analysis depend on the context and the management problem in question. Therefore, we propose a sequence of steps to follow when quantitatively evaluating environmental management against biodiversity.     

Applying a weed risk assessment approach to GM crops

Current approaches to environmental risk assessment of genetically modified (GM) plants are modelled on chemical risk assessment methods, which have a strong focus on toxicity. There are additional types of harms posed by plants that have been extensively studied by weed scientists and incorporated into weed risk assessment methods. Weed risk assessment uses robust, validated methods that are widely applied to regulatory decision-making about potentially problematic plants. They are designed to encompass a broad variety of plant forms and traits in different environments, and can provide reliable conclusions even with limited data. The knowledge and experience that underpin weed risk assessment can be harnessed for environmental risk assessment of GM plants. A case study illustrates the application of the Australian post-border weed risk assessment approach to a representative GM plant. This approach is a valuable tool to identify potential risks from GM plants.     

The development of appropriate ecological criteria and indicators for community forest conservation using participatory methods: A case study in northeastern Thailand

The paper reports the development and evaluation of relevant local ecological criteria and indicators for participatory resource management of community forest. The study site, the Nong Meg-Nong Hee community forest, Maha Sarakham Province, is in northeastern Thailand. Multi-criteria decision-making (MCDM) was adopted as a decision-making tool to evaluate criteria and indicators, using coarse and fine screening, based on local community participation. The criteria and indicators set consisted of 3 principles, 7 criteria, and 41 indicators covering the main attributes of forest ecosystem conservation. Relevant data were processed in a hierarchical framework and used as a template for further assessment, i.e., Principle 1 (forest ecosystem structure and composition), Principle 2 (forest ecosystem function), and Principle 3 (disturbance signs). The two selection phases comprised (1) the coarse screening based on scoring and ranking, and (2) fine screening, using a pair-wise comparison analysis to classify the order of relative weights of the indicators (importance value) and the consistency index (CI) of each criterion. As a result of coarse screening one criterion and 16 indicators were eliminated from the analysis, while the remaining 3 principles, 6 criteria, and 25 indicators were retained for fine screening. Most criteria showed an acceptance value of less than 10% (tolerance consistency index threshold level). The final set of criteria and indicators, based on locally understandable ecological concepts of forest conservation, was ranked in order of importance under each criterion and applied to the study area. We conclude that these techniques are appropriate for selecting criteria and indicators, as they are relatively transparent, understandable and offer an input to participatory decision-making.     

基于随机森林模型的国家重点保护陆生脊椎动物物种优先保护区的识别

准确可靠地识别国家重点保护陆生脊椎动物物种的优先保护区,是生物多样性保护的热点问题之一。采用随机森林(random forests)模型,基于12个环境变量,对中国263种国家重点保护陆生脊椎动物建模,并预测各个物种在背景点的适生概率,迭加计算得到国家重点保护陆生脊椎动物物种的生境适宜性指数。此外,基于对生境适宜性指数的空间自相关分析,识别和确定国家重点保护陆生脊椎动物物种优先保护区,并对优先保护区目前的被保护情况进行分析。结果表明,国家重点保护陆生脊椎动物物种的优先保护区的面积为103.16万km~2,约占我国国土面积的10.90%。优先保护区主要分布在我国的西部地区,包括西南地区的秦岭-大巴山山区、云南省与印度及缅甸的交界地区、武陵山山区、喜马拉雅山-横断山脉山区、阿尔泰山脉山区、天山山脉山区、昆仑山山脉山区;东北的大、小兴安岭、东北-华南沿海地区及长江中下游地区有少量分布。优先保护区中被保护的面积为50.40万km~2,占优先保护区总面积的48.86%,保护率偏低,未被充分保护。利用系统聚类分析,将未被保护的优先保护区划分成3种优先保护顺序,以期为相关部门的决策提供科学依据,更好地保护生物多样性。