The impacts of knowledge of the past on preferences for future landscape change

In this paper, we investigate whether people's knowledge of the past influences their preferences and values towards future landscape change. "Knowledge of the past" is one aspect of the information set held by individuals, and a well-established finding in economics is that changes in information can change preferences and values. The particular aspects of knowledge of the past we work with here are: (i) awareness of past landuse, as represented by woodland cover and (ii) awareness of differing and sometimes contradictory literary impressions of this past landscape. The case studies used here relate to prospective changes in woodland cover in two UK national parks, the Lake District and the Trossachs. We find that people who are made aware that the landscape has changed over time, or that perceptions of the landscape have changed over time, are more likely to favour changes to the current landscape (are less likely to favour the status quo). Knowledge of the past therefore seems to have an impact on preferences for future landscapes. We also investigate the impacts on preferences of how "special", how "wild" and how "worked in" people perceive the landscapes of these two national parks to be.     

A Comparison of Data Sets Varying in Spatial Accuracy Used to Predict the Occurrence of Wildlife-Vehicle Collisions

Wildlife-vehicle collisions (WVCs) pose a significant safety and conservation concern in areas where high-traffic roads are situated adjacent to wildlife habitat. Improving transportation safety, accurately planning highway mitigation, and identifying key habitat linkage areas may all depend on the quality of WVC data collection. Two common approaches to describe the location of WVCs are spatially accurate data derived from global positioning systems (GPS) or vehicle odometer measurements and less accurate road-marker data derived from reference points (e.g., mile-markers or landmarks) along the roadside. In addition, there are two common variable types used to predict WVC locations: (1) field-derived, site-specific measurements and (2) geographic information system (GIS)-derived information. It is unclear whether these different approaches produce similar results when attempting to identify and explain the location of WVCs. Our first objective was to determine and compare the spatial error found in road-marker data (in our case the closest mile-marker) and landmark-referenced data. Our second objective was to evaluate the performance of models explaining high- and low-probability WVC locations, using congruent, spatially accurate (<3-m) and road-marker (<800-m) response variables in combination with field- and GIS-derived explanatory variables. Our WVC data sets were comprised of ungulate collisions and were located along five major roads in the central Canadian Rocky Mountains. We found that spatial error (mean ± SD) was higher for WVC data referenced to nearby landmarks (516 ± 808 m) than for data referenced to the closest mile-marker data (401 ± 219 m). The top-performing model using the spatially accurate WVC locations contained all explanatory variable types, whereas GIS-derived variables were only influential in the best road-marker model and the spatially accurate reduced model. Our study showed that spatial error and sample size, using road-marker data for ungulate species, are important to consider for model output interpretation, which will impact the appropriate scale on which to apply modeling results. Using road-marker references <1.6 km or GPS-derived data locations may represent an optimal compromise between data acquisition costs and analytical performance. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Understanding and Managing Experiential Aspects of Soundscapes at Muir Woods National Monument

Research has found that human-caused noise can detract from the quality of the visitor experience in national parks and related areas. Moreover, impacts to the visitor experience can be managed by formulating indicators and standards of quality as suggested in park and outdoor recreation management frameworks, such as Visitor Experience and Resource Protection (VERP), as developed by the U.S. National Park Service. The research reported in this article supports the formulation of indicators and standards of quality for human-caused noise at Muir Woods National Monument, California. Phase I identified potential indicators of quality for the soundscape of Muir Woods. A visitor “listening exercise” was conducted, where respondents identified natural and human-caused sounds heard in the park and rated the degree to which each sound was “pleasing” or “annoying.” Certain visitor-caused sounds such as groups talking were heard by most respondents and were rated as annoying, suggesting that these sounds may be a good indicator of quality. Loud groups were heard by few people but were rated as highly annoying, whereas wind and water were heard by most visitors and were rated as highly pleasing. Phase II measured standards of quality for visitor-caused noise. Visitors were presented with a series of 30-second audio clips representing increasing amounts of visitor-caused sound in the park. Respondents were asked to rate the acceptability of each audio clip on a survey. Findings suggest a threshold at which visitor-caused sound is judged to be unacceptable, and is therefore considered as noise. A parallel program of sound monitoring in the park found that current levels of visitor-caused sound sometimes violate this threshold. Study findings provide an empirical basis to help formulate noise-related indicators and standards of quality in parks and related areas.     

医用电气设备的环境试验

国家标准GB9706.1-2007《医用电气设备第1部分：安全通用要求》中10.1和10.2明确规定了医用电气设备的运输和储存、运行等环境条件,具体试验方法则依据国家标准GB/T14710-93《医用电气设备环境要求及试验方法》。     

R值评分方法的再学习

详细介绍了目前最常用的预报效能评价R值评分方法的基本内容和不同公式 ,分析了计算R值评分时所选用参数和使用公式不同对R值的影响。以便能正确应用R值评分方法并对R值预报效能评价结果有一清晰的认识     

Accuracy assessment of a small-area method for estimating the spatial distribution of the degree of tree damage

Tree damage, gauged by the amount of defoliation, is one of the basic criteria used to determine treatments for protected and economic forests. Monitoring should include an assessment of the degree of tree damage in different spatial scales. Therefore, in addition to the commonly applied large-area methods, small-area methods should be used. The aim of the paper is to present the results of the accuracy assessment of a small-area method, proposed by Podlaski (2005) [Podlaski, R. (2005). Inventory of the degree of tree defoliation in small areas. Forest Ecology and Management, 215, 361–377], for monitoring the degree of tree damage. The degree of tree damage was shown in sub-blocks P₃ of the system of information on natural environment (SINUS). To estimate the spatial distribution of the degree of tree defoliation, survey sampling, based on simple random sampling with replacement (SRSWR), was used. The degree of damage to fir (Abies alba Mill.) and beech (Fagus sylvatica L.) was analysed in the Święty Krzyż forest section in the Świętokrzyski National Park. The maximum total estimation errors for the proportion of trees with a degree zero of damage, and with second and third degrees of damage together (for α = 0.05) were at most 30.8% for fir and 24.3% for beech trees. For standard, small-area evaluations, these are satisfactory values. In the Święty Krzyż forest section, the number of P₃ sub-blocks with 0.00–5.00% of undamaged trees and with 80.01–100.00% of moderately- or severely-damaged trees was significantly greater for fir than for beech. These results indicate that the fir population was unhealthier than the beech group in the study area. P₃ sub-blocks of the SINUS system, in which the proportion of the healthiest trees was highest, were situated at the forest margin, bordering on meadows and arable fields (in the case of fir) and forming dense patches consisting of several sub-blocks, or occurring singly in the whole study area (in the case of beech). The results show the significant differentiation of forest tree health in small areas.