Sensitivity Analyses of Spatial Population Viability Analysis Models for Species at Risk and Habitat Conservation Planning

Abstract:  Population viability analysis (PVA) is an effective framework for modeling species- and habitat-recovery efforts, but uncertainty in parameter estimates and model structure can lead to unreliable predictions. Integrating complex and often uncertain information into spatial PVA models requires that comprehensive sensitivity analyses be applied to explore the influence of spatial and nonspatial parameters on model predictions. We reviewed 87 analyses of spatial demographic PVA models of plants and animals to identify common approaches to sensitivity analysis in recent publications. In contrast to best practices recommended in the broader modeling community, sensitivity analyses of spatial PVAs were typically ad hoc, inconsistent, and difficult to compare. Most studies applied local approaches to sensitivity analyses, but few varied multiple parameters simultaneously. A lack of standards for sensitivity analysis and reporting in spatial PVAs has the potential to compromise the ability to learn collectively from PVA results, accurately interpret results in cases where model relationships include nonlinearities and interactions, prioritize monitoring and management actions, and ensure conservation-planning decisions are robust to uncertainties in spatial and nonspatial parameters. Our review underscores the need to develop tools for global sensitivity analysis and apply these to spatial PVA.     

生命周期清单分析的数据质量评价

生命周期清单分析(LCI)数据质量的分析方法可概括为两类:采用诸如地理代表性、数据年代或数据获取方式等一系列指标来表示;根据不确定性来综合反映LCI质量.笔者在分析了这两类方法各自所存在缺陷的基础上,提出了将这两者相结合的评价方法:采用5个独立的反映数据质量的指标,根据系统各单元各数据属性对各指标从1～5进行打分,形成数据的质量指标向量元素.根据数据质量向量元素的算术平均在总指标范围中所占的百分数将质量指标向量转化为对应的综合数据质量指标(DQI),继而根据DQI可得出每个数据的随机分布,以便进行清单结果的不确定性随机模拟.最后将方法应用于钢铁生产生命周期清单数据中.      

The role of internal and external information in foraging decisions of Melipona workers (Hymenoptera: Meliponinae)

Social insect foragers have to make foraging decisions based on information that may come from two different sources: information learned and memorised through their own experience (“internal” information) and information communicated by nest mates or directly obtained from their environment (“external” information). The role of these sources of information in decision-making by foragers was studied observationally and experimentally in stingless bees of the genus Melipona. Once a Melipona forager had started its food-collecting career, its decisions to initiate, continue or stop its daily collecting activity were mainly based upon previous experience (activity on previous days, the time at which foraging was initiated the day(s) before, and, during the day, the success of the last foraging flights) and mediated through direct interaction with the food source (load size harvested and time to collect a load). External information provided by returning foragers advanced the start of foraging of experienced bees. Most inexperienced bees initiated their foraging day after successful foragers had returned to the hive. The start of foraging by other inexperienced bees was stimulated by high waste-removal activity of nest mates. By experimentally controlling the entries of foragers (hence external information input) it was shown that very low levels of external information input had large effect on the departure of experienced foragers. After the return of a single successful forager, or five foragers together, the rate of forager exits increased dramatically for 15 min. Only the first and second entry events had large effect; later entries influenced forager exit patterns only slightly. The results show that Melipona foragers make decisions based upon their own experience and that communication stimulates these foragers if it concerns the previously visited source. We discuss the organisation of individual foraging in Melipona and Apis mellifera and are led to the conclusion that these species behave very similarly and that an information-integration model (derived from Fig. 1) could be a starting point for future research on social insect foraging. Received: 16 April 1997 / Accepted after revision: 30 August 1997     

Agricultural Chemicals in the Alluvial Aquifer of a Typical County of the Arkansas Delta

Statistical methods and a Geographic Information System (GIS) were used to investigate potential indicators of ground water vulnerability to agricultural chemical contamination in a representative area of the Mississippi River alluvial aquifer. A total of 47 wells were sampled for analysis of nitrate, phosphorus, potassium, and 13 pesticides commonly-used in the area. Ten soil and hydrogeologic variables and five ground water vulnerability indices were examined to explain the variations of chemical concentrations. The results showed that no individual soil or hydrogeologic variables or their linear combinations could explain more than 25% of the variation of the chemical concentrations. A quadratic response surface model with the values of confining unit thickness, slope, soil permeability, depth to ground water, and recharge rate accounted for 62% of the variation of nitrate, 43% of P, and 83% of K, suggesting that the interactions among soil and hydrogeologic variables were significant. Observed trends of decreasing nitrate and P concentrations with increasing well depth and/or depth to ground water seemed to correlate with carbonate equilibrium in the aquifer and more reduced environment with depth. In view of uncertainties involved, it was recognized that the limitations associated with input data resolution used in GIS and the formulation of leaching indices limited their use for predicting ground water vulnerability. Misuse of pesticides could be another factor that would complicate the relationships between pesticide concentrations and the vulnerability indices.     

METHODOLOGIES FOR CALIBRATION AND PREDICTIVE ANALYSIS OF A WATERSHED MODEL1

ABSTRACT: The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.     

INVESTMENT SEQUENCING RECOGNIZING EXTERNALITIES IN WATER DESALTING1

ABSTRACT. The failure to recognize the learning process in new technologies such as desalting may lead to incorrect water resource investment decisions for two reasons. First, to neglect cost reductions stemming from “learning by doing” implies an overestimation of desalting costs. Second, since learning in a particular plant may result in external (learning) benefits to other plants, these may serve as the basis for a subsidy intended to internalize such benefits. Accordingly, the research reported below includes an estimation of learning functions for desalting and the results of a formulation designed to measure external benefits on the basis of these learning functions. These results are then incorporated into a decision framework for water resource investments which recognizes uncertainty in determining optimal timing of desalting construction.     

油气钻井作业对地下水污染治理研究＊

分析了钻井作业对地下水污染的特点、污染途径,针对性地提出了钻井作业对地下水污染的预防措施,以及污染物处理技术,其中氧化还原技术、气体抽取技术、环境生物技术主要用于有机物污染治理,SRB技术、废弃钻井液固化技术、钻井液转换为水泥浆(MTC)的固井技术可用于处理废弃钻井液。钻井引起的污染治理应以"预防为主,防治结合",切实推行清洁生产和HSE管理。     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

城市非点源污染模型研究进展

回顾了城市非点源污染模型的发展历史,分析了能够模拟城市非点源污染的7个国外模型(SWMM、STORM、SLAMM、HSPF、DR3M-QUAL、MOUSE和HydroWorks)的特点、适用性和局限性,介绍了国外城市非点源污染模型不确定性研究方法和成果以及城市非点源污染分析概率模型,总结了国内城市非点源污染模型的研究成果.指出国外城市非点源污染模型在污染物累积和冲刷、泥沙和污染物运移、污染物的生化反应等方面模拟能力不足,而国内城市非点源污染模型多是经验模型,模拟面积较小,模拟精度较差.提出未来城市非点源污染模型研究应提高泥沙和污染物的模拟能力,探索无资料和不完全信息下城市非点源污染的模拟和预测,加强城市非点源污染随机性模型的研究,发展城市非点源污染模型与GIS的耦合应用.     

原子吸收分光光度法测定水中铅的不确定度评定

对GB/T 7475-1987《水质铜、铅、锌、镉的测定》〔1〕原子吸收分光光度法测定水中铅的测量不确定度进行了评定。通过测量重复性、滴定管、移液管、标准溶液浓度等影响测量结果的不确定度分量的分析和量化,求得水中铅测定结果的相对合成标准不确定度。