Passive and active adaptive management: approaches and an example

Adaptive management is a framework for resource conservation that promotes iterative learning-based decision making. Yet there remains considerable confusion about what adaptive management entails, and how to actually make resource decisions adaptively. A key but somewhat ambiguous distinction in adaptive management is between active and passive forms of adaptive decision making. The objective of this paper is to illustrate some approaches to active and passive adaptive management with a simple example involving the drawdown of water impoundments on a wildlife refuge. The approaches are illustrated for the drawdown example, and contrasted in terms of objectives, costs, and potential learning rates. Some key challenges to the actual practice of AM are discussed, and tradeoffs between implementation costs and long-term benefits are highlighted.     

Adaptive management of natural resources--framework and issues

Adaptive management, an approach for simultaneously managing and learning about natural resources, has been around for several decades. Interest in adaptive decision making has grown steadily over that time, and by now many in natural resources conservation claim that adaptive management is the approach they use in meeting their resource management responsibilities. Yet there remains considerable ambiguity about what adaptive management actually is, and how it is to be implemented by practitioners. The objective of this paper is to present a framework and conditions for adaptive decision making, and discuss some important challenges in its application. Adaptive management is described as a two-phase process of deliberative and iterative phases, which are implemented sequentially over the timeframe of an application. Key elements, processes, and issues in adaptive decision making are highlighted in terms of this framework. Special emphasis is given to the question of geographic scale, the difficulties presented by non-stationarity, and organizational challenges in implementing adaptive management.     

Adaptive management in the U.S. National Wildlife Refuge System: science-management partnerships for conservation delivery

Adaptive management is an approach to recurrent decision making in which uncertainty about the decision is reduced over time through comparison of outcomes predicted by competing models against observed values of those outcomes. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service is a large land management program charged with making natural resource management decisions, which often are made under considerable uncertainty, severe operational constraints, and conditions that limit ability to precisely carry out actions as intended. The NWRS presents outstanding opportunities for the application of adaptive management, but also difficult challenges. We describe two cooperative programs between the Fish and Wildlife Service and the U.S. Geological Survey to implement adaptive management at scales ranging from small, single refuge applications to large, multi-refuge, multi-region projects. Our experience to date suggests three important attributes common to successful implementation: a vigorous multi-partner collaboration, practical and informative decision framework components, and a sustained commitment to the process. Administrators in both agencies should consider these attributes when developing programs to promote the use and acceptance of adaptive management in the NWRS.     

Use of decision analysis interviews to support the sustainable use of the forests in Finnish Upper Lapland

Controversy between alternative uses of forests in Finnish Upper Lapland has been going on for decades, and in recent years it has been escalated to a serious conflict. The core of the conflict is the adverse impacts of forestry on old forests which are important grazing areas for reindeer and which are regarded as intact nature and wilderness areas. This paper describes the experiences of applying multi-criteria decision analysis interview approach on this conflict. The approach provides tools for structuring the problem and preferences of the stakeholders as well as for analyzing the effects of different alternatives in a common framework. We focus on the practical experiences gained from the application of this approach in this context. Multi-criteria decision analysis was found to be a useful approach to evaluate the economic, ecological and cultural aspects of this intense conflict. The obtained experiences also support the view that the approach works best when tightly integrated into the planning process.     

Impact of closed-loop network configurations on carbon footprints: A case study in copiers

Growing concerns about the environment make a supply chains’ eco-footprint increasingly important, presuming that footprints are a more effective (policy) instrument than those currently in use. The eco-footprint comprises all kinds of environmental impact, but often is narrowed down to one aspect; e.g. the carbon footprint, material footprint, the water footprint, and so on. Although returns give rise to an additional goods flow from customers back to producers, it usually improves the eco-footprint due to the substitution effect. The reverse channel supplies high quality (recovered) products, components and materials to the forward channel thereby reducing the need for virgin sourcing and production. We refer to this as closed-loop recovery, as opposed to recovery for cascade markets which lacks substitution. To maximize substitution, the recovered items must re-enter the original supply chain. The feasibility of closed-loop recovery depends partly on the geographical proximity of forward and reverse facilities. We develop a decision framework for optimizing closed loop network configurations, i.e. the combined disposition and location–transport decision. We apply the framework to a single case study concerning one type of footprint (namely the carbon footprint) of a copier (closed-loop) supply chain. The main implication is that a regional network, with combined forward and reverse facilities per continent, proves most efficient and most robust in view of uncertain exogenous variables, but only when a full set of closed-loop options is available (including closed-loop recycling). As an embedded case, main contribution value of it lies in the discovery of a new phenomenon with generic implications; namely that not only the closed-loop supply chains footprint strongly depends on the substitution effect, but that in turn the feasibility of closed-loop recovery options depends heavily on the network design. From delineations of the study we derive issues for further research.     

云南省洱海流域农业生态补偿机制研究

围绕洱海流域种植业产业结构调整、畜禽废弃物资源化利用问题,依托项目示范对农业生态补偿机制进行研究。以大理州政府为补偿主体,示范区内农户为补偿对象,结合资金补贴、技物补偿、智力扶持等补偿途径,开展测土配方施肥、发酵床生态养猪、稻田养鱼、牛粪秸秆混种双孢菇等典型农业生态补偿模式研究,提出了1套农业生态补偿办法。针对补贴力度不足、补贴涵盖领域窄、缺少科学评估方法等问题,分别从政策立法、项目实施、理论研究、试点示范4个层面提出逐步构建洱海流域农业生态补偿机制的建议。     

化学事故危害后果评估与应急救援的辅助决策系统设计

根据重大城市的化学事故后果评估与应急救援的实际需要出发,提出了研制辅助决策系统任务需求和设计构想,阐述了该系统的系统结构、系统实现的流程和系统功能,并做了可行性分析。系统的模块包括：化学品基础信息模块、化学事故类型划分模块、地理信息系统模块、化学源周围道路交通模块、实时的天气预报模块、数学模型模块、危害后果评估模块、救援方案生成模块、人员疏散撤离方案生成模块、人机交互模块。系统与地理信息系统（G IS）、卫星导航系统（GPS）、遥感成像技术结合后能够实现精确定位、快速评估。将物联网技术与系统结合,还可实现对大型固定化学源远程时时监控,确保在事故发生后第一时间掌握信息,为后果评估和应急救援赢得时间,将化学突发事故的危害降到最低。该系统在信息化条件下,较好地为决策者进行化学事故后果评估,为应急救援决策提供了依据,可大幅度提高应急救援效能。     

煤工尘肺患者生存质量与家庭功能和社会支持的关系研究

为了解煤工尘肺患者的生存质量的现状及其与家庭功能和社会支持之间的关系,采用SF-36量表、家庭功能评定量表和社会支持评定量表对212名煤工尘肺患者进行问卷调查。单因素分析显示,煤工尘肺患者在SF-36量表的躯体疼痛、总体健康、活力、情感职能、精神健康5个维度的得分与对照组的差异有统计学意义（p〈0.05）。生存质量得分与家庭功能得分呈负相关,生存质量得分与社会支持得分呈正相关。以生存质量得分为因变量,以家庭功能和社会支持的分量表得分为自变量进行多元逐步回归分析显示,生存质量的影响因素为社会支持利用度、家庭功能的总的功能、情感介入、社会支持的客观支持和问题解决。研究结果表明,煤工尘肺患者生存质量较低,与其家庭功能和社会支持有密切关系,需要从家庭功能和社会支持采取综合措施,提高煤工尘肺患者的生存质量。     

Estimating the vulnerability of fifteen tree species under changing climate in Northwest North America

In the Pacific northwestern (PNW) region of North America, climatic conditions have significantly warmed since a predominantly cool phase of the Pacific North American circulation patterns between 1950 and 1975. What are the implications of this shift in climate for the vulnerability of native tree species? To address this question, we combined mechanistic and statistical models to assess where a variety of native tree species might be more vulnerable within their recorded ranges and where they might potentially migrate. For long-lived species that are well adapted to compete, seasonal differences in photosynthesis and water use offer insights helpful in predicting their distributions. To evaluate the general response of conifers to climatic variation across the region, we previously applied a process-based model (3-PG), to simulate the growth and maximum leaf area index that Douglas-fir could attain within recognized forested areas. We then constructed automated decision tree models to define and map the ecological distributions of 15 tree species based on differences in how photosynthesis was constrained by drought, daytime temperatures, high evaporative demand, and the frequency of frost. For the baseline climate period (1950-1975), the decision tree models predicted presence and absence of each species at ∼23,000 observations with an average accuracy of 81%, with an average kappa statistic of 0.74. In this paper the same models were run annually for the period between 1976 and 2006 for each species, and the areas defined as remaining suitable or becoming vulnerable to disturbance were identified based on whether more or less than half of the years fell within the originally defined limits. Based on these criteria, 70% of the species recorded ranges remained suitable, with 30% deemed vulnerable. Results varied notably by species with western red cedar and western hemlock remaining highly adapted, with potential for range expansion in area of up to 50% relative to the baseline period. In contrast, ponderosa pine, lodgepole pine, grand, and noble fir were classified as vulnerable with potential net contractions in their ranges. The analysis was extended through the rest of the 21st century using climatic projections from the Canadian global circulation model with a high fossil fuel emission scenario (A2) and compared to other previously published species range predictions.     

基于遗传算法的支持向量机预测有机物自燃点的研究

根据定量构效关系(QSPR)原理,研究自燃点(AIT)与其分子结构间的内在定量关系。以265种有机化合物作为样本集,随机选择238种作为训练集,27种作为测试集,用遗传算法(GA)进行变量选择,分别建立多元线性回归(MLR)模型和支持向量机(SVM)模型研究有机物的自燃点与其分子结构间的关系。通过分析,发现造成模型预测效果不佳的原因是试验数据本身存在问题。通过对2个模型的比较,结果为GA-SVM模型明显优于GA-MLR模型,说明自燃点与其分子结构间具有很强的非线性关系。