Using simulation to explore the functional relationships of terrestrial carnivore population indices

Population indices based on visits to detection stations commonly are used to monitor wildlife populations. Inferences about populations are based on 1 of 2 measures: (1) change in the proportion of stations visited at least once or (2) change in the cumulative number of visits by unique individuals. The functional relationships between index responses and population density is poorly understood and can lead to misinterpretation of index data when an incorrect functional relationship (e.g. linear) is assumed. We created a flexible simulation environment to study the response of detection-based population indices under a wide variety of conditions meant to reflect species life history and study design. Proportional indices exhibited non-linear saturating responses to changes in population density while cumulative indices responded linearly. Shapes of responses were functions of home range sizes, individual detection probabilities, and spatial arrangement of animals and sampling stations. Non-linear relationships of proportional indices lead to under-estimation of mean population density when data are aggregated from multiple detection stations deployed in a heterogeneous landscape. Cumulative indices have significant statistical advantages over proportional indices including smaller sample sizes required to detect density change, linearity, consistent index responses across a wide range of densities, and ability to aggregate data to meet minimum sample size requirements. Our simulation provides a flexible tool for the interpretation of station-based population indices.     

Estimating dyad association probability under imperfect and heterogeneous detection

In animal behaviour studies, association indices estimate the proportion of time two individuals (i.e. a dyad) spend in association. In terms of dyads, all association indices can be interpreted as estimators of the probability that a dyad is associated. However, traditional indices rely on the assumptions that the probability to detect a particular individual (p) is either approximately one and/or homogeneous between associated and not associated individuals. Based on marked individuals we develop a likelihood based model to estimate the probability a dyad is associated (ψ) accounting for p < 1 and possibly varying between associated and not associated individuals. The proposed likelihood based model allows for both individual and dyadic missing observations. In addition, the model can easily be extended to incorporate covariate information for modeling p and ψ. A simulation study showed that the likelihood based model approach yield reasonably unbiased estimates, even for low and heterogeneous individual detection probabilities, while, in contrast, traditional indices showed moderate to strong biases. The application of the proposed approach is illustrated using a real data set collected from a population of Commerson's dolphin (Cephalorhynchus commersonii) in Patagonia Argentina. Finally, we discuss possible extensions of the proposed model and its applicability in animal behaviour and ecological studies.     

Analysis of behavioral changes of zebrafish (Danio rerio) in response to formaldehyde using Self-organizing map and a hidden Markov model

Two computational methods were applied to classification of movement patterns of zebrafish (Danio rerio) to elucidate Markov processes in behavioral changes before and after treatment of formaldehyde (0.1 mg/L) in semi-natural conditions. The complex data of the movement tracks were initially classified by the Self-organizing map (SOM) to present different behavioral states of test individuals. Transition probabilities between behavioral states were further evaluated to fit Markov processes by using the hidden Markov model (HMM). Emission transition probability was also obtained from the observed variables (i.e., speed) for training with the HMM. Experimental transition and emission probability matrices were successfully estimated with the HMM for recognizing sequences of behavioral states with accuracy rates in acceptable ranges at central and boundary zones before (77.3-81.2%) and after (70.1-76.5%) treatment. A heuristic algorithm and a Markov model were efficiently combined to analyze movement patterns and could be a means of in situ behavioral monitoring tool.     

保护层分析中独立保护层的识别研究

为阐述保护层分析(LOPA)中独立保护层(IPL)的识别规则,以及这些规则在实际应用中要注意的问题,以生产聚氯乙烯(PVC)的间歇聚合反应为例,对8个不同的LOPA场景进行分析,给出不同场景的IPL和要求时的失效概率(PFD),以及建议增加的IPL。分析结果表明,在进行IPL的识别时,应重点确认IPL的有效性和独立性。在评估IPL有效性时,应关注具有共同元件的IPL,IPL的行动能力、人员行动有效性及IPL的PFD等。在评估独立性时,应确保IPL独立于初始事件和同一场景中的其他IPL的任何构成元件。通过分析,发现PVC工艺中安全阀(PSV)设计、安全仪表功能(SIF)设计和人员行动等IPL中存在的问题,并提出相应的建议。     

危险化学品泄漏事故点火概率取值研究

为计算危险化学品泄漏后发生火灾、爆炸等事故的概率,提高风险评估的量化水平,对点火概率的取值进行研究。从模型计算和直接取参考值2个方面探讨点火概率的取值问题,提出不同情形下的取值建议,并结合案例应用对点火概率2种取值方法进行对比分析研究。研究结果表明:点火概率模型计算不确定性较大且适用性有限,而直接取值的参考值均由一系列事件统计而得出,具有一定的科学性,实际取值更倾向于根据经验数据或已有的数据进行取值,但取值时需要充分考虑实际情况(如安全条件等)进行适当的修正,以便取值更科学。     

化工园区泄漏事故安全风险评估模式研究

为减少化工事故频发造成严重人员伤亡及财产损失,在分析危化品泄漏事故类型的基础上,从事故发生概率和事故后果两方面提出危化品泄漏事故风险评估模型.以中毒事故为例,对重庆长寿化工园区内某企业一液氨储罐进行风险评估.基于概率模型计算中毒事故概率,结果表明该储罐发生泄漏引起中毒的概率较小;数值模拟结果显示:影响范围随时间的增加而...     

基于APJE-SLIM的海运人因失误概率的确定

安全是海运行业永恒的主题,调查研究表明,人因失误是造成海事的主要原因。为了对海运人因失误进行研究,探讨引起人因失误的行为形成因子(PSF),确定在PSF影响下的人因失误概率。在调查海上避让行为的人因失误和这些失误的行为形成因子的基础上,采用APJE和SLIM相结合的方法对航海人员避让行为中的可靠性进行分析。结果表明,航海人员疲劳与健康程度、知识、经验与培训水平、任务复杂程度、安全管理水平与组织有效性等PSF对人因失误概率有着不同程度的影响,相应提高PSF水平,可极大地减少人因失误概率。利用APJE确定端点绝对失误概率,解决了SLIM方法中难以获得参考点概率的问题,获得了在不同种类不同水平PSF影响下的海运人因失误概率。     

基于概率影响图的R&D项目风险传导评估模型

为实现R&D项目风险的有效管理,需要对风险的传导特性进行量化评估。基于传导能量理论,利用概率影响图方法,从拓扑层、函数层和数值层构建了R&D项目风险传导评估模型。分析表明:该模型对项目风险传导量化研究具有较强的适用性;数据采集简便,模型的评估精度取决于信息的完全性;通过应用过程中的模型简化,在一定程度上能够提高模型的实用性。最后考虑项目管理者的决策因素,在评估模型基础上设计了R&D项目风险传导评估框架。     

基于重要抽样法的耐压球壳可靠性计算方法研究

目的 解决耐压球壳极小失效概率的可靠性计算问题。方法 在自适应Kriging的基础上,结合重要抽样法提出耐压球壳可靠性计算方法。该方法在较大失效概率下构建的Kriging模型基础上获得重要方向,在重要方向上计算得到较低失效概率下的设计点,以设计点为中心,构建小失效概率的Kriging模型,并通过此模型采用重要抽样法开展可靠性计算。结果 分别使用提出的重要抽样法和蒙特卡洛法计算了2个算例的失效概率,计算结果表明,该方法具有较高的精度和效率。使用该方法对某耐压球壳工作载荷下的失效概率进行了计算,计算得到该球壳失效概率为4.094×10^–96。结论 研究结果可为无失效方程下极低失效概率的可靠性计算问题提供参考。     

Large-sample-size assessment of socioeconomic predictors of community-level resource management occurrence

Community-level resource management efforts are cornerstones in ensuring sustainable use of natural resources. Yet, understanding how community characteristics influence management practices remains contested. With a sample size of ≥725 communities, we assessed the effects of key community (i.e., socioeconomic) characteristics (human population size and density, market integration, and modernization) on the probability of occurrence of fisheries management practices, including gear, species, and spatial restrictions. The study was based in Solomon Islands, a Pacific Island country with a population that is highly dependent on coastal fisheries. People primarily dwell in small communities adjacent to the coastline dispersed across 6 island provinces and numerous smaller islands. We used nationally collected data in binomial logistic regression models to examine the likelihood of management occurrence, given socioeconomic context of communities. In contrast to prevailing views, we identified a positive and statistically significant association between both human population size and market integration and all 3 management practices. Human population density, however, had a statistically significant negative association and modernization a varied and limited association with occurrence of all management practices. Our method offers a way to remotely predict the occurrence of resource management practices based on key socioeconomic characteristics. It could be used to improve understanding of why some communities conduct natural resource management activities when statistical patterns suggest they are not likely to and thus improve understanding of how some communities of people beat the odds despite limited market access and high population density.