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.     

Perceived mastery climate,felt trust,and knowledge sharing

Interpersonal trust is associated with a range of adaptive outcomes, including knowledge sharing. However, to date, our knowledge of antecedents and consequences of employees feeling trusted by supervisors in organizations remains limited. On the basis of a multisource, multiwave field study among 956 employees from 5 Norwegian organizations, we examined the predictive roles of perceived mastery climate and employee felt trust for employees' knowledge sharing. Drawing on the achievement goal theory, we develop and test a model to demonstrate that when employees perceive a mastery climate, they are more likely to feel trusted by their supervisors at both the individual and group levels. Moreover, the relationship between employees' perceptions of a mastery climate and supervisor‐rated knowledge sharing is mediated by perceptions of being trusted by the supervisor. Theoretical contributions and practical implications of our findings are discussed.     

基于支持向量回归机的南黄海浒苔分布面积预测模型

绿潮作为一种新型的海洋灾害,已经引起了各个国家的重视.依据2012年南黄海海域浒苔遥感监测分布面积数据,选取了温度、天气状况、风向、风力、浪高5种影响浒苔扩散的气候因子,建立了基于SVR的浒苔分布面积预测模型,并与经典的最近邻点插值模型、线性插值模型、3次样条函数插值模型和分段3次Hermite插值模型进行了回归效果的对比.分析结果表明,基于SVR的浒苔分布面积预测模型能够为浒苔遥感数据的插补提供一种方法,且回归效果优于传统的回归方法,为浒苔的防治提供辅助决策信息.     

宁夏地区光伏发电环境效益分析及建模

在阐述了宁夏地区光伏发电发展的优势及发展现状的基础上，分析了光伏发电开发利用对宁夏生态环境产生的积极和消极的影响，并为降低消极影响提出了相应的对策；为了定量直观的评估光伏发电积极影响和消极影响所产生的环境效益，分别建立了节能减排、防风固沙和土地增值、景观效应、植被恢复费用、噪声污染超标罚款、光污染费用等价值模型及光伏发电总的生态环境效益价值模型；最后以中电投宁夏能源铝中卫香山光伏电站为例进行计算验证，结果表明模型的合理性和准确性，同时光伏电站的节能减排效益显著。相对于传统的火力发电，表现了优越的环境效益。     

火灾发生率空间分异及影响因素研究

为了探究我国火灾空间聚集特征与影响因素的空间异质性，采用全局莫兰指数、局部莫兰指数、逐步回归模型、地理加权回归模型、地理探测器方法对我国地级市单元进行研究。研究结果表明:我国火灾发生率具有显著的聚集性；我国火灾发生率较低的“冷点”区域有1个，火灾发生率较高的“热点”区域有4个；人均GDP、城镇居民人均可支配收入、人口密度、年平均气温4 个因素的影响效应具有空间异质性。人均GDP与火灾发生率为正相关，另外3影响因素对火灾发生率的作用表现出正负2种相关关系；2因素交互作用要比单因素作用于火灾发生率时影响力更显著，各影响因素的交互作用类型有非线性增强型和双因子增强型2种。     

Investigating cumulative effects across ecological scales

Species, habitats, and ecosystems are increasingly exposed to multiple anthropogenic stressors, fueling a rapidly expanding research program to understand the cumulative impacts of these environmental modifications. Since the 1970s, a growing set of methods has been developed through two parallel, sometimes connected, streams of research within the applied and academic realms to assess cumulative effects. Past reviews of cumulative effects assessment (CEA) methods focused on approaches used by practitioners. Academic research has developed several distinct and novel approaches to conducting CEA. Understanding the suite of methods that exist will help practitioners and academics better address various ecological foci (physiological responses, population impacts, ecosystem impacts) and ecological complexities (synergistic effects, impacts across space and time). We reviewed 6 categories of methods (experimental, meta-analysis, single-species modeling, mapping, qualitative modeling, and multispecies modeling) and examined the ability of those methods to address different levels of complexity. We focused on research gaps and emerging priorities. We found that no single method assessed impacts across the 4 ecological foci and 6 ecological complexities considered. We propose that methods can be used in combination to improve understanding such that multimodel inference can provide a suite of comparable outputs, mapping methods can help prioritize localized models or experimental gaps, and future experiments can be paired from the outset with models they will inform.     

不同类型压力对飞行员安全绩效的影响规律研究

为探究各类型压力对飞行员安全绩效的影响规律，为飞行员日常工作管理提供依据，选取某航空公司200名飞行员进行压力测试，并调取其近6 a的飞机品质(QAR)数据，通过相关性分析和线性回归分析将飞行员压力数据与其QAR数据进行对比分析，探究飞行员压力对飞行安全绩效的影响规律。研究结果表明:工作负荷压力、家庭压力、人际关系压力和组织管理压力会显著负向影响飞行员安全绩效；角色压力和专业发展压力会显著正向影响飞行员安全绩效。     

Risk assessment and risk reduction of an acrylonitrile production plant

Reducing accident occurrence in petrochemical plants is crucial, thus appropriately allocating management resources to safety investment is a vital issue for corporate management as international competition intensifies. Understanding the priority of safety investment in a rational way helps achieve this objective.In this study, we targeted an acrylonitrile plant. First, Dow Chemical's Fire and Explosion Index (F&EI) identified the reaction process as having the greatest physical risk. We evaluated the severity of accidents in the reaction process using the Process Safety Metrics advocated by the Center for Chemical Process Safety (CCPS); however, this index does not express damages a company actually experience. To solve this problem, we proposed a new metric that adds indirect cost to CCPS metrics. We adopted fault tree analysis (FTA) as a risk assessment method. In identifying top events and basic events, we attempted to improve the completeness of risk identification by considering accidents from the past, actual plant operation and equipment characteristics, natural disasters, and cyber-attacks and terrorist attacks. Consequently, we identified the top events with high priority in handling because of serious accidents as fire/explosion outside the reactor, fire/explosion inside the reactor, and reactor destruction. The new CCPS evaluation index proposed in this study found that fire and explosion outside the reactor has the highest severity. We considered the creation of the fault tree (FT) diagram of the top event, estimating the occurrence probability, and identifying the risk reduction part and capital investment aimed at risk reduction. As an economically feasible selection method for risk reduction investment, using the difference in loss amounts before and after safety investments indicated investment priority.     

Estimating Potential Climate Change Effects on the Upper Neuse Watershed Water Balance Using the SWAT Model

Climate change poses water resource challenges for many already water stressed watersheds throughout the world. One such watershed is the Upper Neuse Watershed in North Carolina, which serves as a water source for the large and growing Research Triangle Park region. The aim of this study was to quantify possible changes in the watershed’s water balance due to climate change. To do this, we used the Soil and Water Assessment Tool (SWAT) model forced with different climate scenarios for baseline, mid‐century, and end‐century time periods using five different downscaled General Circulation Models. Before running these scenarios, the SWAT model was calibrated and validated using daily streamflow records within the watershed. The study results suggest that, even under a mitigation scenario, precipitation will increase by 7.7% from the baseline to mid‐century time period and by 9.8% between the baseline and end‐century time period. Over the same periods, evapotranspiration (ET) would decrease by 5.5 and 7.6%, water yield would increase by 25.1% and 33.2%, and soil water would increase by 1.4% and 1.9%. Perhaps most importantly, the model results show, under a high emission scenario, large seasonal differences with ET estimated to decrease by up to 42% and water yield to increase by up to 157% in late summer and fall. Planning for the wetter predicted future and corresponding seasonal changes will be critical for mitigating the impacts of climate change on water resources.     

Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

Weather variability has the potential to influence municipal water use, particularly in dry regions such as the western United States (U.S.). Outdoor water use can account for more than half of annual household water use and may be particularly responsive to weather, but little is known about how the expected magnitude of these responses varies across the U.S. This nationwide study identified the response of municipal water use to monthly weather (i.e., temperature, precipitation, evapotranspiration [ET]) using monthly water deliveries for 229 cities in the contiguous U.S. Using city‐specific multiple regression and region‐specific models with city fixed effects, we investigated what portion of the variability in municipal water use was explained by weather across cities, and also estimated responses to weather across seasons and climate regions. Our findings indicated municipal water use was generally well‐explained by weather, with median adjusted R² ranging from 63% to 95% across climate regions. Weather was more predictive of water use in dry climates compared to wet, and temperature had more explanatory power than precipitation or ET. In response to a 1°C increase in monthly maximum temperature, municipal water use was shown to increase by 3.2% and 3.9% in dry cities in winter and summer, respectively, with smaller changes in wet cities. Quantifying these responses allows urban water managers to plan for weather‐driven variability in water use.