Adaptive Management and Watersheds: A Social Science Perspective1

Abstract:  Adaptive management is often proposed as the most effective way to manage complex watersheds. However, our experience suggests that social and institutional factors constrain the search for, and integration of, the genuine learning that defines adaptive management. Drawing on our work as social scientists, and on a guided panel discussion at a recent AWRA conference, we suggest that watershed‐scale adaptive management must be recognized as a radical departure from established ways of managing natural resources if it is to achieve its promise. Successful implementation will require new ways of thinking about management, new organizational structures and new implementation processes and tools. Adaptive management encourages scrutiny of prevailing social and organizational norms and this is unlikely to occur without a change in the culture of natural resource management and research. Planners and managers require educational, administrative, and political support as they seek to understand and implement adaptive management. Learning and reflection must be valued and rewarded, and fora established where learning through adaptive management can be shared and explored. The creation of new institutions, including educational curricula, organizational policies and practices, and professional norms and beliefs, will require support from within bureaucracies and from politicians. For adaptive management to be effective researchers and managers alike must work together at the watershed‐scale to bridge the gaps between theory and practice, and between social and technical understandings of watersheds and the people who occupy and use them.     

新疆二氧化硫排放环境学习曲线及减排潜力研究

基于2005—2010年SO2排放量和经济发展数据,对该时段的SO2排放环境负荷变化进行分析并以此数据构建了新疆三大地区万元产值SO2排放量随人均GDP变化的环境学习曲线;以所建立的环境学习曲线为依据,分析2005—2010年万元产值SO,的减排潜力,并对新疆“十二五”期间不同地区万元SO2的节能减排潜力进行了预测。结果表明：经济发展水平越高的地区,万元产值SO2排放环境负荷越小,减排潜力越小;经济发展水平越低的地区,万元产值SO2排放环境负荷越大,减排潜力也越大。研究结果将为“十二五”自治区SO2排放总量控制及减排指标提供科学依据。     

Effect of blue tit song syntax on great tit territorial responsiveness – an experimental test of the character shift hypothesis

A previous study of geographic variation in blue tit (Parus caeruleus) song structure showed that changes in blue tit song syntax (i.e. presence/absence of a trill) are correlated with the breeding density of a close competitor, the great tit (P. major), and are not correlated with other environmental factors such as vegetation structure or blue tit breeding density. We tested the hypothesis that blue tit trilled song represents a character shift that evolved because it reduced territorial interactions with more dominant great tits. We conducted five sets of playback trials in three study populations (mainland southern France, Corsica and Denmark) presenting male great tits with blue tit trilled and untrilled songs and great tit songs. We found that great tits respond equally strongly to both blue tit untrilled songs and to great tit songs, but show a significantly weaker response to blue tit trilled songs. These findings are the first experimental evidence that interspecific competition may play an important role in macrogeographic variation of bird song. Received: 10 October 1999 / Received in revised form: 2 April 2000 / Accepted: 15 April 2000     

The influence of a real job on upper limb performance in motor skill tests: which abilities are transferred?

To investigate whether the specificities of real jobs create distinctions in the performance of workers in different motor tests for the upper limbs, 24 participants were divided into two groups according to their specific job: fine and repetitive tasks and general tasks. Both groups reproduced tasks related to aiming movements, handling and strength of the upper limbs. There were no significant differences between groups in the dexterity and performance of aiming movements. However, the general tasks group had higher grip strength than the repetitive tasks group, demonstrating differences according to job specificity. The results suggest that a particular motor skill in a specific job cannot improve performance in other tasks with the same motor requirements. The transfer of the fine and gross motor skills from previous experience in a job-specific task is the basis for allocating training and guidance to workers.     

Measurement, Collaborative Learning and Research for Sustainable Use of Ecosystem Services: Landscape Concepts and Europe as Laboratory

Policies at multiple levels pronounce the need to encompass both social and ecological systems in governance and management of natural capital in terms of resources and ecosystems. One approach to knowledge production and learning about landscapes as social–ecological systems is to compare multiple case studies consisting of large spaces and places. We first review the landscape concepts’ biophysical, anthropogenic, and intangible dimensions. Second, we exemplify how the different landscape concepts can be used to derive measurable variables for different sustainability indicators. Third, we review gradients in the three dimensions of the term landscape on the European continent, and propose to use them for the stratification of multiple case studies of social–ecological systems. We stress the benefits of the landscape concepts to measure sustainability, and how this can improve collaborative learning about development toward sustainability in social–ecological systems. Finally, analyses of multiple landscapes improve the understanding of context for governance and management.     

Agrodiversity v.2: An educational simulation tool to address some challenges for sustaining functional agrodiversity in agro-ecosystems

Functional agrodiversity can be useful and even essential for, i.e., the long-term sustainability of agriculture. However, still many aspects of this concept are not well understood. The interplay between species in diverse agro-ecosystems is based on processes as, i.e., competition, facilitation, and predator-prey relations. The net-effect of these processes on crop growth is not static and can change over time as the relative density of species change. The equilibrium state of a diverse agro-ecosystem might be far from optimum or even unproductive. This makes agrodiveristy a concept which is not easily grasped nor obtained or maintained. We believe that an agent-based model can facilitate learning on the topic of functional agrodiversity. In this paper, we present the agent-based simulation model, Agrodiversity v.2, developed in Netlogo 3.1.5. The model simulates a virtual diverse agro-ecosystem with four ecological agents. The user is challenged to explore ecological parameters and design a productive sustainable system. The model's “simplest playing level” shows that a proper balance between the co-existing species is necessary so that their ecological interactions allow the multi-species system to become self-organized and persist over time. It demonstrates the transient nature of profitable functional agrodiversity. Our analysis on the effects of using Agrodiversity v.2 on actual learning shows that the learning took place. Students increased the quality of their answers to paper-based individual questions on the topic from 29% during passive/conceptual teaching to 86% after the simulation session. On average students stated to have learnt 55% of their current knowledge through the workshop of which 76% was learnt by using the simulation.     

Automated detection of frog calls and choruses by pulse repetition rate

Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts. Analyzing such data sets will require robust and efficient tools that can automatically identify the presence of a species in audio recordings. Like bats and birds, many anuran species produce distinct vocalizations that can be captured by autonomous acoustic recorders and represent excellent candidates for automated recognition. However, in contrast to birds and bats, effective automated acoustic recognition tools for anurans are not yet widely available. An effective automated call-recognition method for anurans must be robust to the challenges of real-world field data and should not require extensive labeled data sets. We devised a vocalization identification tool that classifies anuran vocalizations in audio recordings based on their periodic structure: the repeat interval-based bioacoustic identification tool (RIBBIT). We applied RIBBIT to field recordings to study the boreal chorus frog (Pseudacris maculata) of temperate North American grasslands and the critically endangered variable harlequin frog (Atelopus varius) of tropical Central American rainforests. The tool accurately identified boreal chorus frogs, even when they vocalized in heavily overlapping choruses and identified variable harlequin frog vocalizations at a field site where it had been very rarely encountered in visual surveys. Using a few simple parameters, RIBBIT can detect any vocalization with a periodic structure, including those of many anurans, insects, birds, and mammals. We provide open-source implementations of RIBBIT in Python and R to support its use for other taxa and communities.     

Learning from the rubble: the case of Christchurch,New Zealand,after the 2010 and 2011 earthquakes

Learning after a disaster is crucial in creating more resilient places. However, many societies are repeatedly overwhelmed by disasters. This can be because of missed opportunities to learn in post‐disaster settings or because of actions implemented that seem to be highly relevant to recovery in the short term, but potentially constrain aspirations in the longer term. This paper assesses learning processes among state and non‐state actors and the ways in which these are bridged and scaled up to wider improvements in governance. Aiming to enrich understanding of post‐disaster learning, it explores different actors’ response actions after the earthquakes in Christchurch, New Zealand, in 2010 and 2011. On the one hand, ‘learning by doing’ is occurring, yet, on the other hand, systemic learning is hindered by mismatches between top‐down steering and bottom‐up initiatives. The study concludes that better linking and synergising of learning processes among different levels is vital for enhancing resilience in post‐disaster societies.     

基于数据驱动的汽车部件温度预测模型研究

目的 基于某汽车在中国吐鲁番地区自然暴露的部件温度变化试验数据,预测该车在美国凤凰城地区气象环境下的汽车部件温度变化。方法 把汽车部件的温度作为输出变量,提取影响汽车部件温度变化的关键特征(试验时间、大气温度、太阳辐照)作为输入变量,同时运用公式对不同纬度地区部件受到的太阳辐照进行修正,以消除地理位置的影响。利用Python等软件构建机器学习模型,用吐鲁番试验数据训练模型,然后预测该车部件在美国凤凰城地区的温度变化。结果 梯度提升机模型具有良好的泛化能力和预测精度,其预测值与实际值的平均绝对误差均在3.3°以内,拟合优度R²均大于0.90。BP神经网络和随机森林算法模型也具有较好的预测精度。结论 利用汽车在我国试验站点进行的自然暴露试验数据,可以预测该汽车部件在国外相似地区气象条件下的温度变化。该研究对于依据汽车部件在我国的自然暴露试验结果预测其他国家相似地区自然环境下汽车部件的温度变化具有一定的指导意义。     

基于自组织映射与随机森林耦合模型的流域水质空间差异性评估

流域水环境质量空间分布特征分析是推进流域精细化管理的基础.本研究基于流域特征指标与水质的关联性,以子流域为分析单元,利用自组织映射人工神经网络模型（SOM）对苕溪流域水质数据聚类分析为3类后与随机森林模型（RF）进行耦合,对全流域水质进行了空间差异性评估.研究结果显示,上游山地区域水质较好,而平原河网人口集聚区的COD_Mn、NH₃-N及TP浓度较高,山地与平原过渡地带水质则主要受到COD_Mn和TN的影响.采用自然环境、社会经济及土地利用/覆盖指标作为流域特征进行水质分级模式识别,SOM与RF模型耦合模型的准确率稳定在80%左右;在对强相关性特征进行筛选识别后,将蒸发蒸腾量、坡度、人口密度、大于10℃积温、旱地占比、城镇用地占比及景观多样性指数为作为输入特征,准确率可达83%,可以有效地开展全流域水质分级评估.