Bayesian Small Area Models for Assessing Wildlife Conservation Risk in Patchy Populations

Abstract:  Species conservation risk assessments require accurate, probabilistic, and biologically meaningful maps of population distribution. In patchy populations, the reasons for discontinuities are not often well understood. We tested a novel approach to habitat modeling in which methods of small area estimation were used within a hierarchical Bayesian framework. Amphibian occurrence was modeled with logistic regression that included third-order drainages as hierarchical effects to account for patchy populations. Models including the random drainage effects adequately represented species occurrences in patchy populations of 4 amphibian species in the Oregon Coast Range (U.S.A.). Amphibian surveys from other locations within the same drainage were used to calibrate local drainage-scale effects. Cross-validation showed that prediction errors for calibrated models were 77% to 86% lower than comparable regionally constructed models, depending on species. When calibration data were unavailable, small area and regional models performed similarly, although poorly. Small area estimation models complement wildlife ecology and habitat studies, and can help managers develop a regional picture of the conservation status for relatively rare species.     

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.     

基于模糊聚类的马氏链模型在交通事故预测中的应用

运用模糊聚类分析方法,借助于MATLAB软件,对中国31个地区交通事故的危害程度进行动态分类和综合评价。根据中国1999—2005年31个地区的交通事故4个指标和5个社会经济因素数据,把31个地区分为3类,即轻灾区、较重灾区、重灾区;其中浙江、福建与宁夏的交通事故危害严重,上海、北京与天津的交通状况正在好转,这与客观实际符合。根据模糊聚类的结果,求得交通事故状态转移概率矩阵,利用马氏链模型可预测未来31个地区的交通事故发展趋势。     

水电厂设备设施风险的动态分级研究

为保障水电厂安全运行,根据水电厂设备设施构成要素,系统分析其风险,建立涵盖水轮机及其辅助设备、发电机及其辅助设备、计算机监控系统及自动装置、电气一次设备、电气二次设备、水工建筑物及金属结构、机具与防护设施等因素在内的评价指标体系。采用欧氏距离定义样本差异,标准化处理属性特征值,通过聚类迭代,开发水电厂设备设施风险的动态分级方法。研究结果表明:水电厂设备设施故障风险在每年的第2,3季度明显大于第1,4季度,并且随着时间推移,各季度设备设施故障风险同比有增加的趋势,风险分级结果与设备设施故障的变化趋势基本吻合。     

阜新市空气监测点位优化的聚类分析

基于聚类分析法利用数据之间距离系数进行分类的原理,建立空气监测点位聚类分析优化模型,结合阜新市地形、气象及历史监测数据,进行阜新市空气监测点位布设优化应用,优化结果表明:距离相似水平取d=0.3时,环保局(B点)与人民公园(C点)监测点空气污染物浓度分布相似性最高,合并为1点,增设气象台监测点位作为清洁背景点,4个点位构成阜新市空气监测新网络;利用CALPUFF模型模拟对优化后监测点位进行相关性检验。检验结果表明:监测点位优化后SO2浓度与实测值相关系数为0.984,PM10相关系数为0.968,NO2相关系数为0.973。CALPUFF模型模拟值与实际监测值之间相关系数均大于0.75,表明优化后的阜新市空气监测点位具有客观环境代表性;监测点位优化与检验方法具有一定应用价值。     

社会资本对农村贫困代际传递影响机制研究

社会资本在反贫困领域的重要作用已被理论和实践所证实。在新时代脱贫攻坚的决胜阶段,有效发挥社会资本的周急济贫功能,缓解乃至阻断贫困代际传递,已成为当前反贫困战略推进中亟待探讨的问题。研究整合了四期中国家庭追踪调查(CFPS 2010、2012、2014、2016)个体家庭和村庄的嵌套数据,建立了“认知型”和“结构型”两种类型以及“个体家庭”和“村庄”两个层级的社会资本框架,并基于探索性因子分析构建了社会网络、社会信任、社会声望、社会参与、集体参与以及公共服务6个社会资本变量。研究利用HLM软件构建了多层广义线性模型(HGLM),通过四阶段的空模型、截距模型、随机效应模型以及完整模型分析了绝对贫困背景下社会资本对农村贫困代际传递的影响机制。研究结果表明:①个体社会资本总量与贫困代际传递呈显著负相关。②在个体家庭层面,社会网络、社会声望、社会参与对贫困代际传递有显著负向影响,但社会信任和地方信任对贫困代际传递的影响并不显著。③在村庄层面,公共服务对贫困代际传递具有显著负向影响,集体参与对贫困代际传递具有显著正向影响,且公共服务水平对社会网络和贫困代际传递具有正向调节作用。研究提出要大力培植贫困群体和乡村两个层级的社会资本以缓解农村贫困代际传递趋势:①要通过协调“乡政”与“村治”以形成良性的乡村治理格局。②要促进精准扶贫与乡村振兴战略紧密衔接,在保障个体脱贫的同时完善农村公共服务及基础设施建设,推进城乡基本公共服务均等化;③须提振贫困群体脱贫信心,增强村级自治与集体参与的水平和能力。     

Effect of Widespread Agricultural Chemical Use on Butterfly Diversity across Turkish Provinces

Although agricultural intensification is thought to pose a significant threat to species, little is known about its role in driving biodiversity loss at regional scales. I assessed the effects of a major component of agricultural intensification, agricultural chemical use, and land‐cover and climatic variables on butterfly diversity across 81 provinces in Turkey, where agriculture is practiced extensively but with varying degrees of intensity. I determined butterfly species presence in each province from data on known butterfly distributions and calculated agricultural chemical use as the proportion of agricultural households that use chemical fertilizers and pesticides. I used constrained correspondence analyses and regression‐based multimodel inference to determine the effect of environmental variables on species composition and richness, respectively. The variation in butterfly species composition across the provinces was largely explained (78%) by the combination of agricultural chemical use, particularly pesticides, and climatic and land‐cover variables. Although overall butterfly richness was primarily explained by climatic and land‐cover variables, such as the area of natural vegetation cover, threatened butterfly richness and the relative number of threatened butterfly species decreased substantially as the proportion of agricultural households using pesticides increased. These findings suggest that widespread use of agricultural chemicals, or other components of agricultural intensification that may be collinear with pesticide use, pose an imminent threat to the biodiversity of Turkey. Accordingly, policies that mitigate agricultural intensification and promote low‐input farming practices are crucial for protecting threatened species from extinction in rapidly industrializing nations such as Turkey. Efectos del Uso Extensivo de Agroquímicos sobre la Diversidad de Mariposas en Provincias Turcas     

北京市PM2.5时空分布特征及其与PM10关系的时空变异特征

PM_(2.5)时空分布特征及其与其它污染物的相关关系是PM_(2.5)时空统计分析的主要研究内容.然而,现有的方法直接从监测站点的角度对时空分布特征进行分析,难以有效地揭示PM_(2.5)浓度的聚集分布特征;同时,常用的地理加权回归在对PM_(2.5)与其它污染物间关系进行建模的过程中,缺乏同时考虑时间异质性与空间异质性,从而不能准确地描述依赖关系的时空变异特征.为此,首先借助于空间聚类分析技术,对北京市2014年PM_(2.5)浓度的聚集结构进行探测,在此基础上,通过聚集结构来分析PM_(2.5)季节性时空分布特征.然后,利用地理时空加权回归对北京市PM_(2.5)与PM_(10)季节平均浓度间关系进行建模,依据回归结果分析PM_(2.5)-PM_(10)间关系的时空变异特征.实验结果表明,春夏季节PM_(2.5)污染程度及空间变异程度均低于秋冬季节,各季节PM_(2.5)浓度均表现为北部浓度低、南部浓度高的空间分布特征;地理时空加权回归具有更好的拟合效果,由回归系数进一步可发现,春夏季PM_(2.5)-PM_(10)相关性低于秋冬季PM_(2.5)-PM_(10)相关性;各季节均表现为西北部PM_(2.5)-PM_(10)的相关性高于东南部PM_(2.5)-PM_(10)的相关性.     

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.