Predicting the unexpected: using a qualitative model of a New Zealand dryland ecosystem to anticipate pest management outcomes

Pest management is expensive and there is often uncertainty about the benefits for the resources being protected. There can also be unintended consequences for other parts of the ecosystem, especially in complex food webs. In making decisions managers generally have to rely on qualitative information collected in a piecemeal fashion. A method to assist decision making is a qualitative modelling approach using fuzzy cognitive maps, a directed graphical model related to neural networks that can take account of interactions between pests and conservation assets in complex food webs. Using all available information on relationships between native and exotic resources and consumers, we generated hypotheses about potential consequences of single‐species and multi‐species pest control on the long‐term equilibrium abundances of other biotic components of an ecosystem. We applied the model to a dryland ecosystem in New Zealand because we had good information on its trophic structure, but the information on the strength of species interactions was imprecise. Our model suggested that pest control is unlikely to significantly boost native invertebrates and lizards in this ecosystem, suggesting that other forms of management may be required for these groups. Most of the pest control regimes tested resulted in greater abundances of at least one other pest species, which could potentially lead to other management problems. Some of the predictions were unexpected, such as more birds resulting from possum and mouse control. We also modelled the effects of an increase in invasive rabbits, which led to unexpected declines of stoats, weasels, mice and possums. These unexpected outcomes resulted from complex indirect pathways in the food web. Fuzzy cognitive maps allow rapid construction of prototype models of complex food webs using a wide range of data and expert opinion. Their utility lies in providing direction for future monitoring efforts and generating hypotheses that can be tested with field experiments.     

Ecological thresholds: Concept,Methods and research outlooks

The concept of ecological thresholds was raised in the 1970s. However, it was subsequently given different definitions and interpretations depending on research fields or disciplines. For most scientists, ecological thresholds refer to the points or zones that link abrupt changes between alternative stable states of an ecosystem. The measurement and quantification of ecological thresholds have great theoretical and practical significance in ecological research for clarifying the structure and function of ecosystems, for planning sustainable development modes, and for delimiting ecological red lines in managing the ecosystems of a region. By reviewing the existing concepts and classifications of ecological thresholds, we propose a new concept and definition at two different levels: the ecological threshold points, i.e. the turning points of quantitative changes to qualitative changes, which can be considered as ecological red lines; the ecological threshold zones, i.e. the regime shifts of the quantitative changes among different stable states, which can be considered as the yellow and/or orange warning boundaries of the gradual ecological changes. The yellow thresholds mean that an ecosystem can return to a stable state by its self-adjustment, the orange thresholds indicate that the ecosystem will stay in the equilibrium state after interference factors being removed, whereas the red thresholds, as the critical threshold points, indicate that the ecosystem will undergo irreversible degradation or even collapse beyond those points. We also summarizes two types of popular Methods in determining ecological thresholds: statistical analysis and modeling based on data of field observations. The applications of ecological thresholds in ecosystem service, biodiversity conservation and ecosystem management research are also reviewed. Future research on ecological thresholds should focus on the following aspects: (1) methodological development for measurement and quantification of ecological thresholds; (2) emphasizing the scaling effect of ecological thresholds and establishment of national-scale observation system and network; and (3) implementation of ecological thresholds as early warning tools in ecosystem management and delimiting ecological red lines.     

基于城市微气候模拟软件的城市道路绿化带对PM2.5消减作用的研究

随着经济的快速发展及机动车保有量的持续增长，车辆造成的道路污染问题日益严重。广州作为中国重要的经济发展城市，交通源排放问题高度集中，机动车排放是城市PM2.5的主要来源之一，开展减缓城市道路污染危害的研究具有重要意义。本研究为调查绿化带对广州城市道路PM2.5的影响，运用实测与城市微气候模拟软件（ENVI-met）模拟结合的研究方法，实测并分析城市道路空间PM2.5的浓度分布及其影响因素，使用实测数据对模拟软件进行验证分析，模拟研究理想道路模型下不同高宽比、风向等因素及绿化带植配类型对PM2.5的消减作用。研究表明：（1）城市道路空间PM2.5浓度分布受污染源、街道高宽比、风速风向、绿化带等综合影响，自然消减情况下，其主要受风速风向和高宽比双因素影响；（2）通常街道高宽比越大，越有利于道路空间PM2.5的扩散；（3）城市道路空间PM2.5自然沉降最小距离为12 m，0-12 m范围内应保持无障碍物的开敞环境，PM2.5消减的关键范围是12-24 m，此范围内可以利用生态手段沉降颗粒物；（4） PM2.5消减率受绿化带和风向的双控制，应根据主导风向选择绿化带植配方式。在主导风平行面和垂直迎风面绿化带对PM2.5有正消减效应，建议植配类型为"乔-乔+灌+草"；在主导风垂直背风面绿化带对PM2.5呈负消减效应，植配类型为"乔-灌"绿化带消减率接近于自然消减率，而植配类型为"乔-灌+草"和"乔-乔+灌+草"的绿化带加重了颗粒物在该区域的积聚。     

西雅图社区花园的用地获取、功能分区和元素组成

社区花园在欧美具有悠久历史,在健康城市建设和促进社区发展方面的功效已经得到证实。目前,已针对社区花园的功能、类型、经营和科普等开展研究,但对土地获取和设计相关的研究较少。西雅图的社区花园历史悠久,并获得政府支持,是美国发展社区花园的城市典范。通过对西雅图23个典型案例进行调研,对用地获取方式、功能类型、分区和元素组成进行分类总结,为设计建造具有中国特色的社区花园提供启示。     

郊野公园植物多样性的生态恢复与重建——以上海滨江森林公园为例

植物作为自然界生态系统的重要组成部分,其多样性的保护与发展是人类可持续发展的物质基础之一,对维护生态环境的平衡与稳定具有重要意义,也是当今中国生态园林城市建设的重要内容。以上海滨江森林公园一期工程为例,通过总结建设过程中自然生境的恢复与重建、现状植物保护和开发的经验,探索郊野公园设计建造中植物多样性保护的有效措施,为今后城市郊野公园的建设和原有绿地的绿化改造等提供参考借鉴。     

基于计算流体力学数值模拟的城市绿地温湿效应及室外热舒适评价研究进展

近年来随着计算机与各学科领域交叉研究的发展,计算流体力学数值模拟方法在城市环境的微气候研究方面得到较多应用,为研究绿地在有限面积内更有效地实现其降温效应提供了新的思路。回顾计算流体力学（CFD）数值模拟方法在不同尺度的城市绿地温湿效应及室外热舒适度评价研究中的应用,在此基础上,总结目前存在的问题及不足,对未来该领域的研究方向提出3点展望,以期为未来城市绿地微气候研究提供参考：1）多平台与尺度扩展研究;2）微气候特征指标的综合交叉分析;3）高适配度模拟模型的及时更新。     

基于机器学习的风景园林智能化分析应用研究

机器学习使实现数据的智能化处理及充分利用数据中蕴含的知识与价值成为可能。探索基于机器学习在风景园林领域智能化分析应用的途径,开展3个实验。其中2个与数据分析研究相关,提出基于调研图像色彩聚类分析的城市色彩印象和基于图像识别技术的景观视觉质量评估与网络应用平台部署实验。最后1个实验与数字化设计创作相关,提出用于设计方案遴选的地形生成方法,包括2个子项目：应用深度学习生成对抗网络（GAN）的地形生成和建立遮罩、预测未知区域的高程。3个实验应用到机器学习中分类、聚类和回归3个主要方向中的算法以及深度学习的生成对抗网络,对传统的研究问题提出了基于机器学习新的研究方法。因此,在应用机器学习风景园林领域,可以有效地从多源数据中学习相互增强的知识,发现问题,并提出解决问题的新方法。     

基于遥感技术的城市下垫面参数与热环境关系的研究——以北京市朝阳区为例

城市热岛（UHI）不仅直接关系到城市人居环境质量和居民健康状况,同时还对城市能源消耗、生态系统过程演变、生物物候以及城市经济可持续发展有着深远的影响。以北京市朝阳区2002—2017年夏季4期Landsat系列遥感影像为数据,采用大气反演法,对15年间朝阳区城市地表温度（LST）时空变化进行分析,发现15年间朝阳区城市热岛比例指数上升迅速,热岛效应逐年加剧。进一步研究表明,城市地表温度与归一化植被指数（NDVI）及归一化建筑指数（NDBI）密切相关：地表温度与NDBI正相关,NDBI指数每升高0.1,地表升温0.79~2.37°C;与NDVI指数负相关,NDVI指数每提高0.1,地表降温0.4~0.77°C。本研究可为城市规划建设与城市绿地营建提供科学具体的参考指导依据,并促进生态可持续发展与人居环境改善。     

城市公园的环境–活动游憩机会谱模型研究——以广州珠江公园为例

城市公园游憩体验的研究对于解决多样化的游憩需求与有限的游憩资源之间的矛盾具有重要意义。将游憩机会谱理论应用于城市公园游憩体验质量改善,提出环境–活动游憩机会谱模型（E-A ROS）。以广州珠江公园为例,采用问卷调查法和SPSS统计分析法,从物质、社会、管理3个方面构建珠江公园环境质量评价的指标体系和指标量化方法,从人口统计学特征、行为特征和行为偏好3个方面进行活动因子研究,探讨珠江公园使用者活动规律,构建珠江公园游憩机会谱。从而为城市公园环境–活动游憩机会谱模型的应用提供了详细的操作指引。