Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Approximating the distribution of recreational visits from on-site survey data

While convenient and often used, on-site surveys are biased by the fact that users who visit the site more often are proportionately more likely to be sampled. This so-called avidity or size biased sampling results in over-estimating the visitation patterns of the average user. This analysis develops a rule of thumb method that may easily be applied by recreation site managers to visitation data collected on-site in order to infer behavior of the average user of the site. The key assumption that drives the derivation is that the visitation data of users is logarithmically distributed. To evaluate the methodology, we analyze several data sets of recreational users assuming that they reflect the populations of users and from these construct hypothetical on-site samples.     

浅析思想政治教育的生态价值

胡锦涛总书记在十七大报告中指出,“建设生态文明,基本形成节约能源资源和保护生态环境的产业结构、增长方式、消费模式”。这一战略思想对中国的发展具有非常重要的意义。没有生态文明,一切文明就没有了享受的前提。思想政治教育不仅对经济政治文化建设有着重要的作用,而且在生态文明的建设过程中依然有重要的作用。加强思想政治教育生态价值的研究,对于缓解我国人口、环境、资源和能源等方面的压力以及我国生态文明建设,落实科学发展观有十分重大的意义。     

空间分辨率与精度协同改进的卫星AOD产品降尺度模型

针对现有卫星气溶胶光学厚度(AOD)产品空间分辨率和精度往往难以满足大气污染精细治理实际需求,提出了一种耦合偏差校正的统计降尺度改进模型(SDBC).该模型基于“空间尺度不变性假设”引入相关驱动因子的额外空间信息实现AOD降尺度,并在此基础上通过偏差校正进一步提升降尺度产品的精度.以1km分辨率MAIAC AOD产品为例,在北京、大湾区、台湾岛3个典型地区开展模型验证.结果表明:(1)DEM、NDVI、人口数量和土地覆盖是影响AOD变化的细节因子,在SDBC空间降尺度过程中引入可将AOD产品的空间分辨率有效提升至500m,且降尺度产品验证R2最高可达0.88;(2)顾及卫星观测几何、质量标识、大气水蒸气柱、气溶胶模式等因子的偏差校正则可进一步提升降尺度AOD产品的精度,3个地区的验证R2均在0.85以上,最高可达0.93;(3)信息熵评估结果显示SDBC模型生成的500m AOD产品提高了原始MAIAC AOD产品的空间信息量.在保留了公里级产品AOD的空间分布格局的基础上,SDBC产品也增强了细节和纹理特征、改善了边界现象和马赛克效应.研究结果证实SDBC模型能有效协同改进现有卫星A...     

高职院校思想政治理论课实践教学探究

2011年教育部一号文件明确规定要把思想政治理论课（以下简称＂思政课＂）实践教学纳入正式教学环节。只有对思政课实践教学进行科学定义,厘清思政课实践教学与其他教学的区别和联系,找准当前存在的问题和不足,提炼出思政课实践教学的特点,进而建立科学规范的教学内容、教学方法、教学形式和考评机制,才能使其落到实处。     

The interrelationships between resource-holding potential,resource-value and reproductive success in territorial males: How much variation can we explain?

A long-standing hypothesis in behavioural ecology posits that males with greater resource-holding potential (RHP) control resource sites deemed more valuable by sexually-receptive females and, thereby, males controlling such sites accrue greater reproductive success (RS). This hypothesis has historically been investigated using three separate but non-mutually exclusive relationships (male RHP vs. resource value, resource value vs. male RS and male RHP vs. RS). The relationships between these three variables are predicted to be strongly positive, however, due to measurement error and biological noise, perfect correlations (r = 1.0) are rare in biology even for well-established relationships. Moreover, the inaccurate identification of either the male trait(s) important to RHP or the resource characteristic sought by females will weaken the observed strength of the relationships. Here, I use meta-analysis to quantitatively describe the general pattern of these relationships in animals. I predict that the relationships between male RHP, resource-value and RS should be significantly positive (male RHP and resource-value should explain a large amount of the variation in male RS). My meta-analysis supports this hypothesis; however, in the best case scenario only ca. 20% of the variation in the response variable was explained. I conclude by identifying areas in which we need to improve our investigations of resource-defence animals and recommending approaches to meet these needs. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social–ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Gender and career success: A typology and analysis of dual paradigms

Despite women's advancements in the workplace, gender inequality persists. We classify and test two frameworks used to explain gender differences in career success: unequal attributes and unequal effects. The unequal attributes framework suggests that gender is related to other attributes, which result in unequal career outcomes for men and women (i.e., a mediated effect). The unequal effects framework suggests that even when men and women share the same attribute or circumstances, they are rewarded differently, such that individual attributes have unequal effects on career outcomes for men and women (i.e., a moderated effect). We collected survey data from a gender‐balanced sample of 394 business school alumni. Using structural equation modeling to test the unequal attributes framework, we found that work hours, career orientation, having a nonemployed spouse, and working in a predominantly female job were unequal attributes that explained gender differences in career success. Using multigroup path analysis to examine unequal effects, we found that being agentic, married, having children at home, and working in a predominantly female workplace had unequal effects in relation to career success for men and women. We find support for both models across three categories of career success antecedents (i.e., personal, family, and job attributes).     

Using the bootstrap with two-phase adaptive stratified samples from multiple populations at multiple locations

Recently the two-phase adaptive stratified sampling design proposed by Francis (1984) has been extended by Manly et al. (2002) for situations where several biological populations are sampled simultaneously, and where this is done at several different geographical locations in order to estimate population totals or means. The method uses the results from a first phase sample to decide how best to allocate a second phase sample to locations and strata, in order to maximise a criterion (based on estimated coefficients of variation) that measures the accuracy of estimation for population totals, for all variables at all locations. One potential problem with this method is bias in the estimators of the population totals and means. In this paper bootstrapping is considered as a means of overcoming these biases. It is shown using model populations of Pacific walrus and shellfish, based on real data, that bootstrapping is a useful tool for removing about half of the bias. This is also confirmed from some simulations using artificial data.