Knowing But Not Doing: Selecting Priority Conservation Areas and the Research–Implementation Gap

Abstract: Conservation assessment is a rapidly evolving discipline whose stated goal is the design of networks of protected areas that represent and ensure the persistence of nature (i.e., species, habitats, and environmental processes) by separating priority areas from the activities that degrade or destroy them. Nevertheless, despite a burgeoning scientific literature that ever refines these techniques for allocating conservation resources, it is widely believed that conservation assessments are rarely translated into actions that actually conserve nature. We reviewed the conservation assessment literature in peer‐reviewed journals and conducted survey questionnaires of the authors of these studies. Two‐thirds of conservation assessments published in the peer‐reviewed scientific literature do not deliver conservation action, primarily because most researchers never plan for implementation. This research–implementation gap between conservation science and real‐world action is a genuine phenomenon and is a specific example of the “knowing–doing gap” that is widely recognized in management science. Given the woefully inadequate resources allocated for conservation, our findings raise questions over the utility of conservation assessment science, as currently practiced, to provide useful, pragmatic solutions to conservation planning problems. A reevaluation of the conceptual and operational basis of conservation planning research is urgently required. We recommend the following actions for beginning a process for bridging the research–implementation gap in conservation planning: (1) acknowledge the research–implementation gap is real, (2) source research questions from practitioners, (3) situate research within a broader conservation planning model, (4) expand the social dimension of conservation assessments, (5) support conservation plans with transdisciplinary social learning institutions, (6) reward academics for societal engagement and implementation, and (7) train students in skills for “doing” conservation.     

Land Community Participation: A New “Public” Participation Model

This essay presents a new “public” participation model that responds to contemporary participation problems. The author extends criticism of a false nature/culture dualism and responds to calls for pragmatic ways to expand participation to an expressive land community. The essay draws on existing scholarship that treats a range of direct to indirect forms of participation in environmental decision-making to argue for and explicate the land community participation model (LCP). The model features participation, power, and chronemics along three distinct and intersecting continua that bind and constitute participatory communication in environmental decision-making. Combining professional field experiences with rhetorical criticism, the author pilots the LCP model in a wilderness dispute resolution case study and suggests additional ways for using LCP as an interpretive guide and design tool in pursuit of environmental democracy.     

To reflect or not to reflect: Prior team performance as a boundary condition of the effects of reflexivity on learning and final team performance

A small but growing body of literature adds to our understanding of the role of team reflexivity (i.e., reflecting upon team functioning) in predicting team performance. Although many studies conclude that reflexivity is an asset for teams, the contingencies of team reflexivity have received far less research attention. In this respect, we argue that team reflexivity may be especially helpful for teams with relatively low performance. Teams that are reflexive tend to learn from previous mistakes, errors, and group processes, which in turn will improve the performance of the team. We propose that this relationship will most likely positively affect learning and final team performance under conditions of relatively poor prior performance. When a team is doing relatively well, the relationship between reflexivity and final team performance will be less clear, as reflexivity and learning is less needed. In a longitudinal study (N = 73 teams), we found support for this idea. As predicted, results indicated that this interaction between team reflexivity and initial team performance on future performance was mediated by team learning. We outline how these findings are important for our understanding of the contingencies of team reflexivity and team performance dynamics. Copyright © 2012 John Wiley & Sons, Ltd.     

基于数据填补-机器学习的煤与瓦斯突出预测效果研究*

为解决煤与瓦斯突出事故数据集少,数据缺失严重的问题,提出将多重插补（MI）和随机森林填补(MF)应用于填补缺失参数,并将填补前和填补后的数据输入SVM,ELM,RF 3种机器学习算法进行训练,构建9种耦合模型。采用总体准确率、局部准确率、运行时间这3种指标评价模型性能。研究结果表明:采用数据填补算法后,由于训练样本增大,煤与瓦斯突出事故预测的总体准确率提高,运行时间增长;MF-RF模型的总体准确率与事故预测准确率最高,分别为97.90%和98.93%;RD-ELM模型的运行时间最短,为0.24 s;多重插补使得煤与瓦斯突出预测的总体准确率提高0.98%~1.11%,随机森林填补总体准确率提高5.13%~7.50%,随机森林填补的效果好于多重插补。     

基于图像感兴趣区域融合特征的PM2.5浓度预测方法

通过图像预测PM_2.5浓度的准确性,在很大程度上取决于模型所选用的特征参数。为丰富特征参数的表达,设计了一种基于图像传统特征与深度特征充分融合的PM_2.5浓度预测方法。首先,根据不同PM_2.5浓度下的成像差异,选定图像感兴趣区域,解决图像尺寸过大导致的模型运算效率较低问题。然后,针对所选取的局部图像,利用传统图像处理方法手动设计并提取图像浅表视觉特征,同时利用卷积神经网络自动提取图像深层语义特征。最后,将两种特征融合,交由卷积神经网络的全连接层实现对PM_2.5浓度的回归预测。预测误差比对结果显示,相比使用单种特征,使用融合特征能够有效提高模型的预测性能。     

A multi-level perspective on learning about climate change adaptation through international cooperation

International cooperation and learning may accelerate climate change adaptation and help countries and regions to adapt more effectively and efficiently. Recognizing the importance and opportunities for mutual learning and knowledge transfer, international and supranational organizations, such as the European Commission, have put programmes for international cooperation in place. This paper presents and tests a framework for assessing multi-level learning outcomes of such international cooperation processes and the conditions that produce these outcomes. The framework distinguishes between: (1) group learning by individual process participants; (2) organizational learning by organizations represented in the process; and (3) network and societal learning by actors external to the process. We verify the analytical potential of the framework by comparing learning by six partners in an adaptation-oriented European cooperation project. The project scores rather high on group learning with participants learning from and – to a lesser extent – also with each other. Learning by partner organizations varied and was generally less whereas learning by external actors was very limited. The case study confirms our expectation that learning outcomes are produced by combinations of partner-specific, process-specific and process-external conditions. The presented framework and insights can be used to stimulate learning in and from international cooperation processes.     

PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS1

ABSTRACT: The cascade correlation neural network was used to predict the two-year peak discharge (Q₂) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q₂ by regression. Our research shows Q₂ was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.     

Developing adaptive capacity within groundwater abstraction management systems

Groundwater is a key resource for global agricultural production but is vulnerable to a changing climate. Given significant uncertainty about future impacts, bottom-up approaches for developing adaptive capacity are a more appropriate paradigm than seeking optimal adaptation strategies that assume a high ability to predict future risks or outcomes. This paper analyses the groundwater management practices adopted at multiple scales in East Anglia, UK, to identify wider lessons for developing adaptive capacity within groundwater management. Key elements are (1) horizontal and vertical integration within resource management; (2) making better use of water resources, at all scales, which vary in space and time; (3) embedding adaptation at multiple scales (from farm to national) within an adaptive management framework which allows strategies and management decisions to be updated in the light of changing understanding or conditions; (4) facilitating the ongoing formation through collective action of local Water Abstractor Groups; (5) promoting efficient use of scarce water resources by these groups, so as to increase their power to negotiate over possible short-term license restrictions; (6) controlling abstractions within a sustainable resource management framework, whether at national (regulatory) or at local (Abstractor Group) scales, that takes account of environmental water needs; and (7) reducing non-climate pressures which have the potential to further reduce the availability of usable groundwater.     

高职《测量技术》“教、学、做”一体化教学模式研究

以高职培养目标为培养依据,分析了目前高职院校《测量技术》课程教学中存在的突出问题。针对问题对《测量技术》课程进行教学改革,按照教学内容模块化、教学任务项目化、理论与实践教学环境一体化、考核方式一体化、师资配备团队化为特色展开实践教学,取得了良好效果。     

流域水质管理优化决策模型研究

针对目前流域水污染难以有效控制的局面,依据已有的流域水文、水动力、水质、水生态相关机理模拟模型的研究,在考虑了流域社会经济发展条件的基础上,构建了流域水质管理优化决策模型框架体系.基于该框架体系,本文从简单流域系统优化模型、模拟与优化联合模型和时空尺度复杂优化模型3个方面对流域优化决策模型的研究发展历程进行综述,并指出其各自在发展过程中所出现的问题.最后,提出了优化决策模型面临的瓶颈问题,并从模型结构简化和适应性管理两个方面提出了相关的解决思路.