Working backwards to move forwards: Graph-based connectivity metrics for reserve network selection

In this paper we apply graph theory in a reserve selection exercise to explore the tradeoffs between maintaining connectivity and minimizing the total area of a protected area network. Rather than focus on a single organism, we used a multi-species approach and looked at the tradeoff curves for organisms with varying dispersal abilities. We first generated the tradeoff curves using a graph-based metric to determine the importance of individual patches for maintaining connectivity. We then performed an analogous set of analyses using patch size as a surrogate measure of importance.     

Applying network theory to prioritize multispecies habitat networks that are robust to climate and land‐use change

Designing connected landscapes is among the most widespread strategies for achieving biodiversity conservation targets. The challenge lies in simultaneously satisfying the connectivity needs of multiple species at multiple spatial scales under uncertain climate and land‐use change. To evaluate the contribution of remnant habitat fragments to the connectivity of regional habitat networks, we developed a method to integrate uncertainty in climate and land‐use change projections with the latest developments in network‐connectivity research and spatial, multipurpose conservation prioritization. We used land‐use change simulations to explore robustness of species’ habitat networks to alternative development scenarios. We applied our method to 14 vertebrate focal species of periurban Montreal, Canada. Accounting for connectivity in spatial prioritization strongly modified conservation priorities and the modified priorities were robust to uncertain climate change. Setting conservation priorities based on habitat quality and connectivity maintained a large proportion of the region's connectivity, despite anticipated habitat loss due to climate and land‐use change. The application of connectivity criteria alongside habitat‐quality criteria for protected‐area design was efficient with respect to the amount of area that needs protection and did not necessarily amplify trade‐offs among conservation criteria. Our approach and results are being applied in and around Montreal and are well suited to the design of ecological networks and green infrastructure for the conservation of biodiversity and ecosystem services in other regions, in particular regions around large cities, where connectivity is critically low.     

Land User Participation in Developing a Computerised Decision Support System for Combating Desertification

Combating desertification in natural rangelands has recently become a priority in large parts of southern Africa. Rangeland managers, farmers, scientists, conservationists and land users have been applying a variety of restoration technologies to address this problem. Bush encroachment, as part of the desertification process, involves the natural replacement of the herbaceous plant cover by undesirable problem woody species. The active and passive restoration technologies that are applied, are mainly based on indigenous knowledge and include the chemical, mechanical or manual reclamation of unproductive rangelands, as well as the combating of woody and alien species encroachment. Indigenous practices and knowledge play a major role in the effectiveness and success rate of these technologies. This project faces the challenge of bringing together both local and scientific knowledge in a single user-friendly, computerised Decision Support System (DSS) which is directly accessible by land users to support them in the process of decision making, concerning the combating of desertification. Case studies from central and northern Namibia were used to combine qualitative and quantitative data to develop this Decision Support System. The DSS currently consists of two databases and an expert system, which evaluates the results of land users’ management practices, and provides easily accessible information and advice for participants in the system, based on the incorporated data. The DSS is also linked to national and international web sites and databases to offer a wider range of information on technologies concerning agricultural and conservation practices.     

A mixed methodology for evaluating use of evidence in conservation planning

Conservation practitioners widely recognize the importance of making decisions based on the best available evidence. However, the effectiveness of evidence use in conservation planning is rarely assessed, which limits opportunities to improve evidence-based practice. We devised a mixed methodology for empirically evaluating use of evidence that applies social science tools to systematically appraise what kinds of evidence are used in conservation planning, to what effect, and under what limitations. We applied our approach in a case study of the Nature Conservancy of Canada (NCC), a leading land conservation organization. We conducted qualitative and quantitative analyses of 65 NCC planning documents (n = 13 in-depth) to identify patterns in evidence use, and surveyed 35 conservation planners to examine experiences of and barriers to using evidence. Although claims in plans contained a wide range of evidence types, 26% of claims were not referenced or associated with an identifiable source. Lack of evidence use was particularly apparent in claims associated with direct threats, particularly those identified as low (71% coded as insufficient or lacking evidence) or medium (45%) threats. Survey participants described relying heavily on practitioner experience and highlighted capacity limitations and disciplinary gaps in expertise among planning teams as barriers to using evidence effectively. We found that although time-intensive, this approach yielded actionable recommendations for improving evidence use in NCC conservation plans. Similar mixed-method assessments may streamline the process by including interviews and refining the document analysis frames to target issues or sections of concern. We suggest our method provides an accessible and robust point of departure for conservation practitioners to evaluate whether the use of conservation planning reflects in-house standards and more broadly recognized best practices.     

Socially robust knowledge in coastal projects

Interactive modes of knowledge production offer a strategy for seeking solutions to complex environmental problems. The outcome of such knowledge production is socially robust knowledge. Social robustness refers to knowledge that is relevant and accepted by actors in the context of its application. This is achieved when knowledge is credible, salient and produced in a legitimate way. To date, only limited research has focused on how social robustness is achieved. As coastal problems are characterised by conflicting interests and major uncertainties, the coastal zone represents a relevant domain for studying socially robust knowledge. This paper analyses and presents three conditions that need to be in place if one is to achieve socially robust knowledge in coastal projects. The conditions are based on theories related to socially robust knowledge, boundary spanning, project arrangements and knowledge arrangements. The conditions specify how social robustness can be achieved through knowledge testing by boundary spanners, the involvement of diverse actors and a close connection between knowledge production and the evolving project. In a case study, these conditions are compared to developments in a Dutch coastal project involving spatial developments near the Ems estuary. The comparison highlights the relevance of the three conditions in achieving socially robust knowledge. In addition, a fourth aspect is empirically uncovered: the role of boundary spanning among project partners prior to producing knowledge. These four conditions clarify how social robustness may be achieved in coastal solutions. As such, this paper contributes to the theoretical and empirical understanding of socially robust knowledge.     

Ambient nitrogen oxides exposure and early childhood respiratory illnesses

Acute respiratory infections are common in children below 5 years and recent studies suggest a possible link with air pollution. In this study, we investigated the association between ambient nitrogen oxides (NO_x) and bronchitis or upper airway inflammation.This longitudinal study was conducted in Teplice and Prachatice districts, Czech Republic. Children were followed from birth to 4.5 years of age. Data were compiled from medical records at delivery and at follow up, and from self-administered questionnaires from the same two time points. Air pollution monitoring data were used to estimate exposure over five different averaging periods ranging from three to 45 days prior to an episode. To quantify the association between exposure and outcome, while accounting for repeated measure correlation we conducted logistic regression analysis using generalized estimating equations.During the first 2 years of life, the adjusted rate ratio for bronchitis associated with interquartile increase in the 30-day average NO_x was 1.31 [95% confidence interval (CI): 1.07, 1.61] and for two to 4.5 year olds, it was 1.23 (95% CI: 1.01, 1.49). The 14-day exposure also had stable association across both age groups: below 2 years it was 1.25 (95% CI: 1.06, 1.47) and for two to 4.5 years it was 1.21 (95% CI: 1.06, 1.39). The association between bronchitis and NO_x increased with child's age in the under 2 years group, which is a relatively novel finding.The results demonstrate an association between NO_x and respiratory infections that are sufficiently severe to come to medical attention. The evidence, if causal, can be of public health concern because acute respiratory illnesses are common in preschool children.     

天津市城区暴雨沥涝仿真模拟系统的研究

天津市位于黄河水系尾闾，是我国北方重要的工业和港口城市。该市历史上一直遭受洪涝灾害的威胁，加之近几年市区范围不断扩大，不透水面积增加，排水能力远远不能适应城市的发展要求，城市沥涝问题日趋严重。为了掌握天津市暴雨沥涝的规律，减轻洪涝灾害对该市的影响，作者利用二维不恒定流水动力学模型和计算机信息管理及图形技术，采用Power Station Fortran 4.0和Visual Basic5.0编程语言，在Widows98环境下开发了天津市暴雨沥涝仿真模拟系统。该系统首次实现了从城市暴雨预报、监测到城市暴雨沥涝仿真模拟的研究， 不仅能够处理实时的不均匀的降雨信息，还能处理数值预报模型的预报降雨信息。系统的信息前后处理模块用图形方式管理仿真模型的各类信息，方便用户显示、查询和修改，使系统更加完整、实用。     

基于知识的AVHRR影像的水体自动识别方法与模型研究

根据水体光谱特性，通过分析水体在ＮＯＡＡ气象卫星的ＡＶＨＲＲ影像上的表象特征及与其它相关环境因子在光谱信息上的差异，认为水体在ＡＶＨＲＲ影像上具有光谱可分辨性，从而提出了基于水体光谱知识的水体自动提取识别的水体描述模型，并将该模型应用于太湖、淮河、渤海等地区，取得较好的效果。该模型方法同样可扩展应用于其它传感器获得的影像数据，从而实现遥感信息提取的自动化与智能化     

ISAPEC中知识自动获取的设计与实现

剖析了利用人工神经网络实现大气污染总量控制规划智能决策支持系统（ＩＳＡＰＥＣ）中知识自动获取的必要性，论述了ＩＳＡＰＥＣ中知识的自动获取的设计与实现过程，并用一实例进行了验证。结果表明用人工神经网络实现知识的自动获取与知识的人工获取相比既具有更高的时间效率，又能保证更高的质量。     

大气污染总量控制规划智能决策支持系统的开发与研究

对大气污染总量控制规划智能决策支持系统的开发步骤和总体结构做了介绍。并描述了该系统中的知识库系统、模型库系统、数据库系统和图形库系统的设计思想,特别是对知识库的构造进行了较详细的叙述。还介绍了神经网络技术在该系统中的应用。指出大气污染总量控制规划智能决策支持系统将为大气污染总量控制提供更科学、更合理、更有效的规划方案。