Cost‐Effectiveness of Using Small Vertebrates as Indicators of Disturbance

In species‐rich tropical forests, effective biodiversity management demands measures of progress, yet budgetary limitations typically constrain capacity of decision makers to assess response of biological communities to habitat change. One approach is to identify ecological‐disturbance indicator species (EDIS) whose monitoring is also monetarily cost‐effective. These species can be identified by determining individual species’ responses to disturbance across a gradient; however, such responses may be confounded by factors other than disturbance. For example, in mountain environments the effects of anthropogenic habitat alteration are commonly confounded by elevation. EDIS have been identified with the indicator value (IndVal) metric, but there are weaknesses in the application of this approach in complex montane systems. We surveyed birds, small mammals, bats, and leaf‐litter lizards in differentially disturbed cloud forest of the Ecuadorian Andes. We then incorporated elevation in generalized linear (mixed) models (GL(M)M) to screen for EDIS in the data set. Finally, we used rarefaction of species accumulation data to compare relative monetary costs of identifying and monitoring EDIS at equal sampling effort, based on species richness. Our GL(M)M generated greater numbers of EDIS but fewer characteristic species relative to IndVal. In absolute terms birds were the most cost‐effective of the 4 taxa surveyed. We found one low‐cost bird EDIS. In terms of the number of indicators generated as a proportion of species richness, EDIS of small mammals were the most cost‐effective. Our approach has the potential to be a useful tool for facilitating more sustainable management of Andean forest systems. Rentabilidad del Uso de Pequeños Vertebrados como Indicadores de Perturbaciones     

Contribution of Urban Expansion and a Changing Climate to Decline of a Butterfly Fauna

Butterfly populations are naturally patchy and undergo extinctions and recolonizations. Analyses based on more than 2 decades of data on California's Central Valley butterfly fauna show a net loss in species richness through time. We analyzed 22 years of phenological and faunistic data for butterflies to investigate patterns of species richness over time. We then used 18–22 years of data on changes in regional land use and 37 years of seasonal climate data to develop an explanatory model. The model related the effects of changes in land‐use patterns, from working landscapes (farm and ranchland) to urban and suburban landscapes, and of a changing climate on butterfly species richness. Additionally, we investigated local trends in land use and climate. A decline in the area of farmland and ranchland, an increase in minimum temperatures during the summer and maximum temperatures in the fall negatively affected net species richness, whereas increased minimum temperatures in the spring and greater precipitation in the previous summer positively affected species richness. According to the model, there was a threshold between 30% and 40% working‐landscape area below which further loss of working‐landscape area had a proportionally greater effect on butterfly richness. Some of the isolated effects of a warming climate acted in opposition to affect butterfly richness. Three of the 4 climate variables that most affected richness showed systematic trends (spring and summer mean minimum and fall mean maximum temperatures). Higher spring minimum temperatures were associated with greater species richness, whereas higher summer temperatures in the previous year and lower rainfall were linked to lower richness. Patterns of land use contributed to declines in species richness (although the pattern was not linear), but the net effect of a changing climate on butterfly richness was more difficult to discern. Contribución de la Expansión Urbana y un Clima Cambiante a la Declinación de la Fauna de Mariposas     

Using citizen science butterfly counts to predict species population trends

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade‐offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011–2014) of a short‐duration citizen science project (Big Butterfly Count [BBC]) with those from long‐running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3‐week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3‐week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short‐duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species’ flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass‐participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass‐participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land‐use types), as well as serving to reconnect an increasingly urban human population with nature.     

结构地震反应性态的物理随机最优控制

本研究发展了结构地震反应性态的随机最优控制理论和方法.这一研究建立在物理随机系统思想的新理论框架下,突破了以It(o)随机微分方程描述动力系统的经典随机最优控制的藩篱.提出了基于系统二阶统计量评价、单目标超越概率和多目标能量均衡的控制器参数设计准则,以及基于概率可控指标的控制器位置设计准则,并将它们统一为物理随机最优控...     

改进广义灰色关联故障树的危化品LSSC可靠性诊断

为了探究影响危化品物流整体运作的关键事故因素,提出一种改进广义关联故障树的危化品LSSC可靠性诊断方法。结合实例构建危化品LSSC系统失效故障树,运用灰色关联技术对以最小割集构造的标准故障模式向量和底事件概率重要度组成的待检模式向量进行相关性分析,得到系统失效概率等级与失效故障模式可能性排序,确定系统关键薄弱环节。研究结果表明:该方法能够快速识别危化品物流运作系统关键事故因素,为危化品LSSC风险等级控制和分级防范措施制定提供参考和借鉴。     

颗粒物污染对高血压门急诊就诊人数的影响-时间序列研究

为探讨颗粒物对金昌市高血压门急诊就诊人数影响的暴露反应关系,本文收集甘肃省金昌市2012年1月1日~2015年12月31日大气PM₁₀、SO₂、NO₂数据及2014年1月1日~2015年12月31日大气PM_2.5污染物监测数据及同期气象观测数据,同时收集近年金昌市三家综合医院的高血压门急诊日就诊病例.采用广义相加模型,分析不同大气污染物与高血压门急诊日就诊人数的关联性.结果表明,在单污染物模型中,滞后L07d时PM₁₀平均浓度每升高一个IQR,高血压日门急诊人数增加2.30%（95% CI：1.30%~3.32%）,L6d时PM_2.5平均浓度每升高一个IQR,高血压日门急诊人数增加2.53%（95% CI：1.45%~3.62%）.PM₁₀和PM_2.5对男性、65岁以上高血压患者门急诊影响更高.SO₂和NO₂与颗粒物之间存在协同效应,沙尘天气下PM₁₀对高血压门急诊人数的影响由2.30%增加到2.36%,PM_2.5的影响由2.53%减少到2.39%.研究得出颗粒物污染对金昌市高血压门急诊就诊人数具有不同程度的影响,其中细颗粒物（PM_2.5）的效应更强.     

大气质量评价的广义对比加权标度指数法

基于大气污染物浓度成等比变化 ,其危害程度成等差变化 ,提出用标度指数表示大气质量的新思想 ;采用对标度分指数的广义对比运算的因子赋权新方法 ,导出大气质量评价的广义对比加权标度指数计算公式。广义对比加权标度指数评价法应用于实例评价结果与用灰色聚类评价法的评价结果比较表明 ,该评价法具有简单性、合理性、可比性和通用性     

GENERALIZED LOW-FLOW FREQUENCY RELATIONSHIPS FOR UNGAGED SITES IN MASSACHUSETTS1

ABSTRACT: Regional hydrologic procedures such as generalized least squares regression and streamflow record augmentation have been advocated for obtaining estimates of both flood-flow and low-flow statistics at ungaged sites. While such procedures are extremely useful in regional flood-flow studies, no evaluation of their merit in regional low-flow estimation has been made using actual streamflow data. This study develops generalized regional regression equations for estimating the d-day, T-year low-flow discharge, Q_{d, t}, at ungaged sites in Massachusetts where d = 3, 7, 14, and 30 days. A two-parameter lognormal distribution is fit to sequences of annual minimum d-day low-flows and the estimated parameters of the lognormal distribution are then related to two drainage basin characteristics: drainage area and relief. The resulting models are general, simple to use, and about as precise as most previous models that only provide estimates of a single statistic such as Q_7,10. Comparisons are provided of the impact of using ordinary least squares regression, generalized least squares regression, and streamflow record augmentation procedures to fit regional low-flow frequency models in Massachusetts.     

Regression analysis of forest damage by marginal models for correlated ordinal responses

Studies on forest damage generally cannot be carried out by common regression models, for two main reasons: Firstly, the response variable, damage state of trees, is usually observed in ordered categories. Secondly, responses are often correlated, either serially, as in a longitudinal study, or spatially, as in the application of this paper, where neighbourhood interactions exist between damage states of spruces determined from aerial pictures. Thus so-called marginal regression models for ordinal responses, taking into account dependence among observations, are appropriate for correct inference. To this end we extend the binary models of Liang and Zeger (1986) and develop an ordinal GEEI model, based on parametrizing association by global cross-ratios. The methods are applied to data from a survey conducted in Southern Germany. Due to the survey design, responses must be assumed to be spatially correlated. The results show that the proposed ordinal marginal regression models provide appropriate tools for analysing the influence of covariates, that characterize the stand, on the damage state of spruce.     

基于灰色关联度的污染物排放主导因素研究——以苏州市为例

应用灰色关联度理论对1991～2007年间影响苏州市工业污染物排放的主导因素进行分析。初步研究结果表明,在分析时段内,规模效应、结构效应以及广义技术效应对苏州工业污染物的排放影响都比较大。对于每一种污染物排放,不同效应影响程度不同。总体上看,分析时段苏州地区工业污染物排放受万元污染物排放系数(技术减排效应)影响最大,GDP总量(规模效应影响)次之,第二产业比例(产业结构效应)最弱。1991～2007经济规模增长对污染增加的贡献十分明显,产业结构调整因素还未产生明显的环境效益。技术进步的作用显著。综合起来,污染排放增加量与GDP增加相比较为缓和。苏州必须充分挖掘技术效应在污染削减中的作用,有效地实施产业结构优化和升级,以确保节能减排任务的实现。