生态系统管理的多目标体系和方法

20世纪90年代以来,人们开始采用“生态系统管理”这种基于生态系统原理的综合方法管理自然资源和生态环境,促进人类与自然的和谐发展。然而,生态系统自身的复杂性、动态性及不确定性特点使得“生态系统管理”难以形成明确的定义和方法体系,系统的多重尺度和目标也增加了管理的难度。近20年兴起的系统工程主要针对大规模复杂系统进行研究,实现系统的总体优化。多目标决策和决策支持系统是系统工程的常用技术方法,生态系统管理是“基于目标”的管理模式。本文概述了生态系统管理的概念和要素,从构建管理体系的角度着重阐述了生态系统管理目标体系的结构、构建过程及目标间的相互关系,针对生态系统的复杂特性探讨了多目标决策方法和决策支持系统在生态系统管理中的应用,以期能够对形成具有普遍意义和实际操作性的生态系统管理方法体系起到借鉴作用。     

Consistent Implementation of Decisions in the Brain

Despite the complexity and variability of decision processes, motor responses are generally stereotypical and independent of decision difficulty. How is this consistency achieved? Through an engineering analogy we consider how and why a system should be designed to realise not only flexible decision-making, but also consistent decision implementation. We specifically consider neurobiologically-plausible accumulator models of decision-making, in which decisions are made when a decision threshold is reached. To trade-off between the speed and accuracy of the decision in these models, one can either adjust the thresholds themselves or, equivalently, fix the thresholds and adjust baseline activation. Here we review how this equivalence can be implemented in such models. We then argue that manipulating baseline activation is preferable as it realises consistent decision implementation by ensuring consistency of motor inputs, summarise empirical evidence in support of this hypothesis, and suggest that it could be a general principle of decision making and implementation. Our goal is therefore to review how neurobiologically-plausible models of decision-making can manipulate speed-accuracy trade-offs using different mechanisms, to consider which of these mechanisms has more desirable decision-implementation properties, and then review the relevant neuroscientific data on which mechanism brains actually use.     

Engineering precision into variable biological systems

Technology change stimulates the expectations of society and seeks to satisfy its demands. Continuing advances in electronics and information technology over the last 20 years have responded to the challenge posed by the environmental impacts of agricultural production systems. Future agriculture will require precision techniques to assemble information and achieve increasingly precise and responsive management practices in order to reduce wasteful inputs, and meet the social and economic pressures for safe high quality food at lower cost. Biosystems engineering, an interdisciplinary science linking biology, physics, engineering and mathematics, can provide understanding and then innovative precision solutions for agriculture. Public and industry investment will be needed to achieve the necessary goals of lower environmental impact, and precision techniques will make a signi. cant contribution. Worldwide, the availability of tools that allow accurate control of inputs and traceable management of food production will contribute to sustainable food production for all. The vision is of systems that utilise sensing methods and mathematical models of the biological process, and link them through to control algorithms that realise practical benefits. Tools to measure spatial variation in crop yield will draw on statistical models to interpret complex variation and provide robust information to justify variable input management. Machine vision will interpret complex natural scenes, leading to real‐time control, and biosensors will be incorporated into automated systems for sensing animal fertility status, leading to automatic monitoring of animal health and welfare.     

Framework for Applying a Complex Adaptive System Approach to Model the Operation of Eco‐Industrial Parks

Models of eco‐industrial parks (EIPs) might help us transform our production systems by fostering the emergence of sustainable EIPs since such models have the potential to support the decision‐making processes of cooperative companies that participate and to decrease operational uncertainties. In this article, a conceptual framework for modeling the operation of EIPs is presented. The framework is underpinned by complex adaptive systems theory, industrial ecology, and an analysis of the experiences of existing EIPs. The proposed framework draws on the observed strengths of two types of industrial symbiosis models—planned eco‐industrial parks (PEIPs) and EIPs that developed through self‐organizing symbiosis (SOS)—as well as their observed weaknesses and the features of complex adaptive systems. From this analysis, five key properties to be modeled are deduced: functionality, reliability, life span, theoretical knowledge, and adaptability. It is proposed that the properties of functionality and theoretical knowledge are determined by the goals of the EIP and its member companies, while the property of adaptability is determined by the understanding that the companies in an EIP have of the environment surrounding the EIP, while the properties of reliability and life span are determined by the internal and external relationships of the companies that make up an EIP.     

矿区可持续生态环境管理规划方法研究

矿区生态规划理论与方法研究可为矿区的可持续发展提供具体的理论指导和技术支持．作为矿区生态规划的重要组成部分之一的矿区可持续的生态环境管理规划,贯穿于矿区整个开发生产过程．以可持续的生态环境建设和管理的思想为基础,建立了矿区生态环境管理体系框架,对生态环境管理指标的确定、生态环境因素的识别以及生态环境管理方案的决策提出了具体的方法．     

Predicting species distributions for conservation decisions

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on‐ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision‐making contexts when used within a structured and transparent decision‐making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of ‘translators’ between modellers and decision makers. We encourage species distribution modellers to get involved in real decision‐making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.     

Integrating cell-level kinetic modeling into the design of engineered protein therapeutics

Functional genomics and proteomics are identifying many potential drug targets for novel therapeutic proteins, and both rational and combinatorial protein engineering methods are available for creating drug candidates. A central challenge is the definition of the most appropriate design criteria, which will benefit critically from computational kinetic models that incorporate integration from the molecular level to the whole systems level. Interpretation of these processes will require mathematical models that are refined in combination with relevant data derived from quantitative assays, to correctly set biophysical objectives for protein design.     

A conceptual framework for understanding the perspectives on the causes of the science–practice gap in ecology and conservation

Applying scientific knowledge to confront societal challenges is a difficult task, an issue known as the science–practice gap. In Ecology and Conservation, scientific evidence has been seldom used directly to support decision‐making, despite calls for an increasing role of ecological science in developing solutions for a sustainable future. To date, multiple causes of the science–practice gap and diverse approaches to link science and practice in Ecology and Conservation have been proposed. To foster a transparent debate and broaden our understanding of the difficulties of using scientific knowledge, we reviewed the perceived causes of the science–practice gap, aiming to: (i) identify the perspectives of ecologists and conservation scientists on this problem, (ii) evaluate the predominance of these perspectives over time and across journals, and (iii) assess them in light of disciplines studying the role of science in decision‐making. We based our review on 1563 sentences describing causes of the science–practice gap extracted from 122 articles and on discussions with eight scientists on how to classify these sentences. The resulting process‐based framework describes three distinct perspectives on the relevant processes, knowledge and actors in the science–practice interface. The most common perspective assumes only scientific knowledge should support practice, perceiving a one‐way knowledge flow from science to practice and recognizing flaws in knowledge generation, communication, and/or use. The second assumes that both scientists and decision‐makers should contribute to support practice, perceiving a two‐way knowledge flow between science and practice through joint knowledge‐production/integration processes, which, for several reasons, are perceived to occur infrequently. The last perspective was very rare, and assumes scientists should put their results into practice, but they rarely do. Some causes (e.g. cultural differences between scientists and decision‐makers) are shared with other disciplines, while others seem specific to Ecology and Conservation (e.g. inadequate research scales). All identified causes require one of three general types of solutions, depending on whether the causal factor can (e.g. inadequate research questions) or cannot (e.g. scientific uncertainty) be changed, or if misconceptions (e.g. undervaluing abstract knowledge) should be solved. The unchanged predominance of the one‐way perspective over time may be associated with the prestige of evidence‐based conservation and suggests that debates in Ecology and Conservation lag behind trends in other disciplines towards bidirectional views ascribing larger roles to decision‐makers. In turn, the two‐way perspective seems primarily restricted to research traditions historically isolated from mainstream conservation biology. All perspectives represented superficial views of decision‐making by not accounting for limits to human rationality, complexity of decision‐making contexts, fuzzy science–practice boundaries, ambiguity brought about by science, and different types of knowledge use. However, joint knowledge‐production processes from the two‐way perspective can potentially allow for democratic decision‐making processes, explicit discussions of values and multiple types of science use. To broaden our understanding of the interface and foster productive science–practice linkages, we argue for dialogue among different research traditions within Ecology and Conservation, joint knowledge‐production processes between scientists and decision‐makers and interdisciplinarity across Ecology, Conservation and Political Science in both research and education.     

Sustainable ecosystem management using optimal control theory: part 1 (deterministic systems)

The concept of sustainability, an abstract one by its nature, has been given a mathematical representation through the use of Fisher information as a measure. It is used to propose the sustainability hypotheses for dynamical systems, which has paved the way to achieve sustainable development through externally enforced control schemes. For natural systems, this refers to the task of ecosystem management, which is complicated due the lack of clear objectives. This work attempts to incorporate the idea of sustainability in ecosystem management. The natural regulation of ecosystems suggests two possible control options, top-down control and bottom-up control. A comparison of these two control philosophies is made on generic food chain models using the objectives derived from the sustainability hypotheses. Optimal control theory is used to derive the control profiles to handle the complex nature of the models and the objectives. The results indicate a strong relationship between the hypotheses and the dynamic behavior of the models, supporting the use of Fisher information as a measure. As regards to ecosystem management, it has been observed that top-down control is more aggressive but can result in instability, while bottom-up control is guaranteed to give a stable and improved dynamic response. The results also indicate that bottom-up control is a better option to affect shifts in the dynamic regimes of a system, which may be required to recover the system from a natural disaster like the hurricane Katrina.     

Music and Video Gaming during Breaks: Influence on Habitual versus Goal-Directed Decision Making

Different systems for habitual versus goal-directed control are thought to underlie human decision-making. Working memory is known to shape these decision-making systems and their interplay, and is known to support goal-directed decision making even under stress. Here, we investigated if and how decision systems are differentially influenced by breaks filled with diverse everyday life activities known to modulate working memory performance. We used a within-subject design where young adults listened to music and played a video game during breaks interleaved with trials of a sequential two-step Markov decision task, designed to assess habitual as well as goal-directed decision making. Based on a neurocomputational model of task performance, we observed that for individuals with a rather limited working memory capacity video gaming as compared to music reduced reliance on the goal-directed decision-making system, while a rather large working memory capacity prevented such a decline. Our findings suggest differential effects of everyday activities on key decision-making processes.     

Operational seasonal forecasting of crop performance

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统（SES）模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现：（1）多数模型对景观过程与社会经济决策的反馈关系分析不足;（2）应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;（3）通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括：集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

Applying voting rules to panel-based decision making in LCA

Background, aim, and scope  Cross-category weighting is one possible way to facilitate internal decision making when dealing with ambiguous impact assessment results, with simple additive weighting being a commonly used method. Yet, the question as to whether the methods applied today can, in fact, identify the most “environmentally friendly” alternative from a group perspective remains unanswered. The aim of this paper is to propose a new method for group decision making that ensures the effective identification of the most preferable alternative. Materials and methods  Common approaches to deduce a single set of weighting factors for application in a group decision situation (e.g., arithmetic mean, consensus) are discussed based on simple mathematics, empirical data, and thought experiments. After proposing an extended definition for “effectiveness” in group decision making, the paper recommends the use of social choice theory whose main focus is to identify the most preferable alternative based on individuals’ rankings of alternatives. The procedure is further supplemented by a Monte Carlo analysis to facilitate the assessment of the result’s robustness. Results  The general feasibility of the method is demonstrated. It generates a complete ranking of alternatives, which does not contain cardinal single scores. In terms of effectiveness, the mathematical structure of the procedure ensures the eligibility for compromise of the group decision proposal. The sensitivity analysis supports the decision makers in understanding the robustness of the proposed group ranking. Discussion  The method is based upon an extended definition of effectiveness which acknowledges the eligibility for compromise as the core requirement in group decision contexts. It is shown that multi-attribute decision-making (MADM) methods in use in life cycle assessment (LCA) today do not necessarily meet this requirement because of their mathematical structure. Further research should focus on empirical proof that the generated group results are indeed more eligible for compromise than results generated by current methods that utilize an averaged group weighting set. This is closely related to the question considering under which mathematical constraints it is even possible to generate an essentially different result. Conclusions  The paper describes a new multi-attribute group decision support system (MGDSS) for the identification of the most preferable alternative(s) for use in panel-based LCA studies. The main novelty is that it refrains from deducing a single set of weighting factors which is supposed to represent the panel as a whole. Instead, it applies voting rules that stem from social choice theory. Because of its mathematical structure, the procedure is deemed superior to common approaches in terms of its effectiveness. Recommendations and perspectives  The described method may be recommended for use in internal, panel-based LCA studies. In addition, the basic approach of the method—the combination of MADM methods with social choice theory—can be recommended for use in all those situations where multi-attribute decisions are to be made in a group context.     

An attempt at decision making in tissue engineering: reactor evaluation using the analytic hierarchy process (AHP)

To develop a method of human-oriented evaluation in tissue engineering and regenerative medicine, we applied the analytic hierarchy process (AHP) in the evaluation of tissue engineering reactors. For evaluating a reactor for a bioartificial liver (BAL) support system, we identified five criteria: safety, scalability, cell growth environment, mimicking native liver functions and handling. Based on these criteria, we evaluated six different types of BAL bioreactors by three panelists. Using the AHP method, we successfully ranked BAL systems for bridge use in liver transplantation in the attempt at decision making based on human-oriented evaluation.     

Automating assistance for safety critical decisions

Computer systems are increasingly being introduced to assist in decision making, including hazardous decision making. To ensure effective assistance, decision procedures should be theoretically sound, flexible in operation (particularly in unpredictable environments) and effectively accountable to human supervisors and auditors. Strengths and weaknesses of classical statistical decision models are discussed from these perspectives. It is argued that more can be learned from human decision behaviour than has traditionally been assumed, and this motivates the concept of a symbolic decision procedure (SDP). The SDP is defined, described in terms of first-order (predicate) logic, and its use illustrated in a decision support system for medicine. We point out that the classical numerical decision procedure is a special case of a generalized symbolic procedure, and discuss the potential for rigorous formalization of the latter. We conclude that symbolic decision procedures may meet requirements for assisting human operators in hazardous situations more satisfactorily than classical decision procedures.     

Indicators of environmentally sound land use in the humid tropics: The potential roles of expert opinion,knowledge engineering and knowledge discovery

Despite abundant literature on indicators for sustainable resource management, practical tools to help local users to apply its general concepts at a local to regional level are scarce. This means that decisions over land evaluation and land use at a local level are often not based on the formal application of indicators or decision support systems for environmentally sound management but instead on the opinion of local expertise, for instance forest managers, cattle breeders, farmers and/or academics. This is particularly seen to be the case in the tropics where access to modern communication and information technologies is restricted.As the opinions of experts are often based on and influenced by personal experience, intuition, heuristics and bias, their evaluations and decision are often unclear to the non-expert working at a local level. In order to make their reasoning more comprehensible to the non-expert, the ecological condition of 176 plots in the tropical Southeast of Mexico were evaluated by experts on soil fertility, forest management, cattle breeding and agriculture. With the assistance of a knowledge engineer (one who converts expert knowledge and reasoning into a model), these expert opinions and reasoning were then translated into a formal computer model.As an alternative approach we applied a knowledge discovery technique, namely the induction of regression trees and automatically developed models using the expert evaluations as training data. Where knowledge engineering was tedious and time consuming, regression models could be rapidly generated. Moreover, the correspondence between regression trees and expert opinions was considerably higher than the correspondence between expert opinion and their own models. The regression trees used less explicative variables than the models generated by the experts. The minimisation of sampling effort due to variable space reduction means that the application of regression tree induction has a high potential for a rapid development of indicators for narrowly defined ecological assessments, needed for decision making on a local or regional scale.     

ModelPROBE: model driven soil probing, site assessment and evaluation

Conventional techniques for site characterisation are time consuming, cost intensive, and often do not support decision making with regard to sustainable remediation. Therefore, new techniques for step by step site characterization with smart feed back loops are necessary that are able to support a future “soil framework directive”. Advanced geophysical site characterization techniques combined with new types of vegetation analysis will be developed. Based on these non-invasive surveys, the extension of sources, contamination levels (THP, BTEX, PAH, CHC, explosives, heavy metals and radio nuclides) and soil heterogeneities will be localized first. Hot spots will then be investigated by new direct push probing systems integrated with geophysical and hydrogeological methods and combined with chemical and isotopic contaminant analysis for source localization and identification (environmental forensics). The actually occurring bioprocesses, such as contaminant degradation or sorption and mobilization processes, will be assessed using biosensors, in situ microcosms, and stable isotope and biomarker analysis. These new techniques and tools will be evaluated against best practice of conventional methods. The ModelPROBE project (EU-FP7 collaborative project) gives the opportunity to test, optimize and demonstrate the proposed approach at fully equipped and characterized European brownfield reference sites in Germany, Italy, Norway and the Czech Republic. Integrated statistical analysis and modelling at different stages, the step by step approach will result in an improved view of soil and subsurface contamination and will provide a sound basis for a cost-effective risk assessment and decision in the choice of the most appropriate sustainable remediation strategy.     

The regulatory circuits for hysteretic switching in cellular signal transduction pathways

Hysteresis can be found in many physical systems, and a hysteretic switch has been used for various mechanical and electrical systems. Such a hysteretic switch can be created by using a single positive feedback loop, as often used in engineering systems. It is, however, intriguing that various cellular signaling systems use coupled positive feedback loops to implement the hysteretic switch. A question then arises about the advantage of using coupled positive feedback loops instead of simple isolated positive feedback for an apparently equivalent hysteretic switch. Through mathematical simulations, we determined that cellular systems with coupled positive feedback loops show enhanced hysteretic switching, and can thereby make a more reliable decision under conditions of noisy signaling. As most intracellular processes are accompanied by intrinsic noise, important cellular decisions such as differentiation and apoptosis need to be highly robust to such noises. The coupled positive feedback loops might have been evolutionarily acquired to enable correct cell fate decisions to be made through enhanced hysteretic switching in noisy cellular environments.