Habitat classification: A comparison using avian species and guilds

Results of breeding bird censuses in 1979 and 1980 were used to compare the relationships of both species and guilds to forest habitats in the White Mountains of New Hampshire. Several age classes of 11 forest cover types were studied: northern hard-woods (Fagus-Betula-Acer), spruce (Picea), spruce-fir (Picea-Abies), birth (Betula), swamp hardwoods (Acer-Pinus-Tsuga), pine (Pinus strobus andP. resinosa), balsam fir (Abies), aspen (Populus tremuloides andP. grandidentata), northern red oak (Quercus), oak-pine (Quercus-Pinus), and hemlock (Tsuga). All types were even-aged; only northern hardwoods had an additional uneven-aged condition. Forest cover types were also pooled to consider generalized habitats: hardwoods, mixed forest, or softwoods. Results of ordinations based on censuses of 74 bird species indicate that foraging guilds are more related to general cover types than are nesting substrate guilds, but bird species reflect habitat differences to a greater degree than do either guild scheme. Also, considerable overlap occurs in bird species distribution between hardwoods and mixed forests; softwoods show little overlap with other types. Discriminant function and classification analyses revealed that bird species composition can be used to correctly classify general forest habitats more accurately (83.8%) than either foraging (63.2%) or nesting substrate guilds (58.4%). These results indicate that, of the habitats studied, avian species compositions are more characteristic than are foraging or nesting substrate guild composition, which tend to be similar across forest habitats.     

MULTISCALE RIVER ENVIRONMENT CLASSIFICATION FOR WATER RESOURCES MANAGEMENT1

ABSTRACT: River Environment Classification (REC) is a new system for classifying river environments that is based on climate, topography, geology, and land cover factors that control spatial patterns in river ecosystems. REC builds on existing principles for environmental regionalization and introduces three specific additions to the “ecoregion” approach. First, the REC assumes that ecological patterns are dependent on a range of factors and associated landscape scale processes, some of which may show significant variation within an ecoregion. REC arranges the controlling factors in a hierarchy with each level defining the cause of ecological variation at a given characteristic scale. Second, REC assumes that ecological characteristics of rivers are responses to fluvial (i.e., hydrological and hydraulic) processes. Thus, REC uses a network of channels and associated watersheds to classify specific sections of river. When mapped, REC has the form of a linear mosaic in which classes change in the downstream direction as the integrated characteristics of the watershed change, producing longitudinal spatial patterns that are typical of river ecosystems. Third, REC assigns individual river sections to a class independently and objectively according to criteria that result in a geographically independent framework in which classes may show wide geographic dispersion rather than the geographically dependent schemes that result from the ecoregion approach. REC has been developed to provide a multiscale spatial framework for river management and has been used to map the rivers of New Zealand at a 1:50,000 mapping scale.     

Landform Classification for Land Use Planning in Developed Areas: An Example in Segovia Province (Central Spain)

Landform-based physiographic maps, also called land systems inventories, have been widely and successfully used in undeveloped/rural areas in several locations, such as Australia, the western United States, Canada, and the British ex-colonies. This paper presents a case study of their application in a developed semi-urban/suburban area (Segovia, Spain) for land use planning purposes. The paper focuses in the information transfer process, showing how land use decision-makers, such as governments, planners, town managers, etc., can use the information developed from these maps to assist them. The paper also addresses several issues important to the development and use of this information, such as the goals of modern physiography, the types of landform-based mapping products, the problem of data management in developed areas, and the distinctions among data, interpretations, and decisions.     

国家级名镇(村)的旅游价值与开发模式探析

阐述了历史文化名镇（村）的概念和意义，并对80个国家级名镇（村）进行了划分，在此基础上分析了历史文化名镇（村）的开发现状与存在的问题。指出在80个历史文化名镇（村）中，有的已经创建了等级旅游区点，或被评为优秀旅游城市等，已将资源优势转化为产品优势；有的目前则少有开发成效。基于区域经济学的增长极、点轴、网络开发理论和可持续发展理念，提出了历史文化名镇（村）可持续开发的模式与建议。     

基于风险管理方法的危险源评价分级研究

以化工企业内涉及危险物质的装置、设施而构成的危险源为研究对象,在简要分析重大危险源评价分级的作用与方法的基础上,针对中小型化工企业,提出基于风险管理方法的危险源评价分级概念。通过计算危险源引发事故的概率和事故后果来确定危险源的风险值,并将其分为4个等级,指导企业制定合适的安全管理制度、使用恰当的安全技术措施,以最小化的代价确保危险源安全运行,从而提高中小型化工企业的危险源管理水平。     

基于事故理论的城市轨道交通风险评价模型研究

笔者分析了城市轨道交通事故,在分析我国其他行业事故分类的基础上,确定城市轨道交通事故分类标准,即重大事故、大事故、险性事故和一般事故,并将不同事故分类情况及专家判断评分,按事故的大小不同换算成可以计算的计算尺度,根据事故种类不同计算出事故折算因子,根据风险理论的评价方法,建立了地铁风险评价模型,对地铁的危险性进行量化定级,并通过具体实例进行综合分析评价,该风险评价模型具有一定的工程意义。     

煤矿危险源辨识与多层评价分级研究

根据煤炭系统生产的复杂特点,提出了采用系统科学的方法,对煤炭生产进行系统的单元划分,采用安全系统科学辨识的方法对危险源进行辨识.在此基础上,提出利用层次分析法和模糊动态评判的方法对危险源进行初次分级,根据危险源的耦合特性,提出二次动态分级的方法,对初次分级进行调整,确定危险源科学、合理的级别,并提出了进一步现实性评价的建议.     

RIVER AMENITY EVALUATION: A REVIEW AND COMMENTARY1

ABSTRACT: During the past 15 years a number of methods have been developed that purport to evaluate the amenity values of rivers. Most methods are designed to identify the physical, biological, cultural, and esthetic features of a river or river corridor that are conducive to recreation, preservation, and other amenity values. This paper reviews and comments on 13 methods that evaluate amenity values. The methods are reviewed and discussed under three general headings: River Recreation Potential Evaluation, River Esthetic Evaluation, and River Preservation Evaluation. A final section of the paper identifies areas where improvements and further research are needed.     

EROSIONAL POTENTIAL OF STREAMS IN URBANIZING AREAS1

ABSTRACT: In urbanizing areas, the usual increase in flood flows also increases erosional capability of streams. In order to evaluate such tendencies quantitatively, 25 stream reaches were studied, and were classified as to whether erosion of the channel and banks was light, medium, or heavy. Analysis of characteristics indicated that (1) densely developed areas are correlated with greater erosion, (2) wide stream buffers of natural vegetation are correlated with lesser erosion, and (3) there is no definite correlation of erosion to slope or characteristics of soil. Erosional stream instability can be avoided by retention of storm water runoff, creating additional channel roughness or reducing channel slope during floods by drop structures, such as culverts, which restrict flow. Channel straightening and general bank protection should be minimized in such streams. Design of culverts should take such effects into consideration.     

An Automated Technique for Delineating and Characterizing Valley-Bottom Settings

Accurate delineation and characterization of valley-bottom settings is crucial to the assessment of the biological and geomorphological components of riverine systems; yet, to date, most valley-bottom mapping endeavors have been done manually. To improve this situation, we developed automated techniques in a Geographic Information System (GIS) for delineating and characterizing valley-bottom settings in river basins ranging in size from approximately 1,000–10,000 km². All procedures were developed with ARC/INFO GIS software and fully automated in Arc Macro Language (AML). The GRID module is required for valley-bottom delineation and slope calculations; whereas characterization (i.e., measuring the width of the valley-bottom zone) requires Coordinate Geometry (COGO) in the ARCEDIT module. The process requires three inputs: a polygon coverage of the analysis area; an arc coverage of its hydrography, and a grid representing its digital elevation. The AML is designed to operate within a wide range of computer memory/disk space options, and it allows users to customize several procedures to match the scale and complexity of a given analysis area with available computer hardware.