Impact of Criticism of Null-Hypothesis Significance Testing on Statistical Reporting Practices in Conservation Biology

Abstract:  Over the last decade, criticisms of null-hypothesis significance testing have grown dramatically, and several alternative practices, such as confidence intervals, information theoretic, and Bayesian methods, have been advocated. Have these calls for change had an impact on the statistical reporting practices in conservation biology? In 2000 and 2001, 92% of sampled articles in Conservation Biology and Biological Conservation reported results of null-hypothesis tests. In 2005 this figure dropped to 78%. There were corresponding increases in the use of confidence intervals, information theoretic, and Bayesian techniques. Of those articles reporting null-hypothesis testing—which still easily constitute the majority—very few report statistical power (8%) and many misinterpret statistical nonsignificance as evidence for no effect (63%). Overall, results of our survey show some improvements in statistical practice, but further efforts are clearly required to move the discipline toward improved practices.     

基于RS和GIS的河北省生物丰度和植被覆盖度归一化系数研究

利用河北省生态环境十年变化（2000—2010）遥感调查与评估项目2010年土地利用类型数据,计算河北省2010年省级生物丰度和植被覆盖指数归一化系数,并对各县市的归一化系数进行分析和研究,绘制2010年河北省生物丰度和植被覆盖指数等级图,清晰反映出全省的区域分布特征,为河北省生态环境影响评价研究提供技术支撑。     

Reliability data acquisition and evaluation in process plants

Based on the practical experience of assessing reliability data in two plants of the explosives industry, the organization and scope of the data collection, the component delimitation, the procedures of quality assurance and the data evaluation for their final use in PSA studies are presented. An example is given for the type of detection and repair, etc. The mathematical background for evaluating observed component lifetimes is discussed. Both frequentist and Bayesian methods are addressed. The resulting failure rate distributions and their approximations by log-normal distributions for several key components are presented. They were obtained using a Bayesian approach with a non-informative prior. Remarks on uncertainty, their treatment and a proposal for transferring data to plants other than the ones investigated conclude the presentation.     

Estimating annual generation rates of total P and total N for different land uses in Tasmania, Australia

Water quality issues have become increasingly important to Australian catchment stakeholders. As extensive nutrient sampling and modelling expertise are often absent or unattainable, simple unit-area models like Catchment Management Support System (CMSS) remain an attractive option for informing water quality management decisions. The selection of nutrient generation rates for use in CMSS is often an arbitrary assignment based on limited literature sources or expert opinion. Using a Bayesian model to estimate nutrient generation rates for the region of Tasmania, Australia, improved the rigor of CMSS modelling and in the process highlighted that dairy pastures were the most significant contributor of total phosphorus and total nitrogen loads to Tasmanian rivers.     

基于贝叶斯网络的汽车起重机液压系统的可靠性评估

为评估汽车起重机液压系统的可靠性,首先结合故障树分析法(FTA)、贝叶斯网络(BN)构建液压系统故障树,然后映射成BN,再利用其在多态性和不确定性方面的优势,应用桶排除法,对系统进行可靠性评估。在给出底事件的发生概率后,通过正向推理,得到顶事件发生的概率为0.191 016。针对模型进行多态性和不确定性修正后,得到的顶事件概率为0.228 073,相对提高19.4%。最后利用反向推理,找出系统的薄弱环节并给出改正措施,为更全面高效的可靠性评估提出一种新方法。     

Predicting Bird Species Distributions in Reconstructed Landscapes

Abstract:  Landscape optimization for biodiversity requires prediction of species distributions under alternative revegetation scenarios. We used Bayesian model averaging with logistic regression to predict probabilities of occurrence for 61 species of birds within highly fragmented box–ironbark forests of central Victoria, Australia. We used topographic, edaphic, and climatic variables as predictors so that the models could be applied to areas where vegetation has been cleared but may be replanted. Models were evaluated with newly acquired, independent data collected in large blocks of remnant native vegetation. Successful predictions were obtained for 18 of 45 woodland species (40%). Model averaging produced more accurate predictions than "single best" models. Models were most successful for smaller-bodied species that probably depend on particular vegetation types. Predictions for larger, generalist species, and seasonal migrants were less successful, partly because of changes in species distributions between model building (1995–1997) and validation (2004–2005) surveys. We used validated models to project occurrence probabilities for individual species across a 12,000-km² region, assuming native vegetation was present. These predictions are intended to be used as inputs, along with landscape context and temporal dynamics, into optimization algorithms to prioritize revegetation. Longer-term data sets to accommodate temporal dynamics are needed to improve the predictive accuracy of models.     

ANN OUTPUT UPDATING OF LUMPED CONCEPTUAL RAINFALL/RUNOFF FORECASTING MODELS1

ABSTRACT: Artificial neural networks (ANNs) are tested for the output updating of one‐day‐ahead and three‐day‐ahead streamflow forecasts derived from three lumped conceptual rainfall/runoff (R‐R) models: the GR4J, the IHAC, and the TOPMO. ANN output updating proved superior to a parameter updating scheme and to the ‘simple’ output updating scheme, which always replicates the last observed forecast error. In fact, ANN output updating was able to compensate for large differences in the initial performance of the three tested lumped conceptual R‐R models, which the other tested updating approaches were not able to achieve. This is done mainly by incorporating input vectors usually exploited for ANN R‐R modeling such as previous rainfall and streamflow observations, in addition to the previous observed error. For one‐day‐ahead forecasts, the performance of all three lumped conceptual R‐R models, used in conjunction with ANN output updating, was equivalent to that of the ANN R‐R model. For three‐day‐ahead forecasts, the performance of the ANN‐output‐updated conceptual models was even superior to that of the ANN R‐R model, revealing that the conceptual models are probably performing some tasks that the ANN R‐R model cannot map. However, further testing is needed to substantiate the last statement.     

广西背景大气中生物质燃烧对碳质气溶胶组成和吸光性的影响

碳质气溶胶是大气细颗粒物（PM_2.5）的重要组成部分,对空气质量、人体健康和气候变化均有重要影响.针对生物质燃烧（BB）这一碳质气溶胶的重要来源,于2017年11月至2018年10月在广西壮族自治区背景地区采集了PM_2.5样品,分析了样品中的碳质组成、糖类化合物和水溶性棕色碳（BrC）的吸光系数（b_abs）.使用气团老化指数（AAM）校正LG浓度以消除LG降解带来的影响,进而结合贝叶斯混合模型与分子示踪剂法量化了BB对有机碳（OC）的贡献率,并通过相关性分析法探讨了BrC的可能来源.结果表明,研究期间AAM指数平均值为0.40±0.28,表示LG存在光化学降解过程.农作物秸秆是广西地区最主要的生物质燃料类型,在未考虑LG降解下,全年玉米、水稻和甘蔗秸秆焚烧排放的OC分别占总OC的22%、23%和18%;考虑LG在大气中的降解后,相对贡献率分别降低至16%、21%和17%.LG的降解会导致BB对OC的贡献率评估被低估,经过AAM指数校正后,全年BB对OC的贡献率平均值为49.0%.水溶性BrC的b_abs全年的平均值为（8.7±10.7） Mm^-1,其中BB、化石燃料燃烧以及初级生物气溶胶排放可能是BrC的重要来源.     

基于贝叶斯优化的三维水动力-水质模型参数估值方法

随着水质目标管理要求的提升,基于复杂的三维水动力-水质模型的决策成为流域精准治理的必需.水质模型通常具有复杂的结构,包含大量的方程和参数,而参数取值的准确性会影响模型对水体系统表征的可靠性,进而影响根据模型结果进行水环境管理的效果,因此,有必要探究适用于复杂水质模型的高效参数估值方法.传统的自动参数估值方法应用于复杂的水质模型时会面临计算瓶颈,而贝叶斯优化适用于高运算成本模型的优化问题.本研究提出基于贝叶斯优化的复杂水质模型参数估值方法,主要包括:①重要影响参数识别;②重要参数敏感性排序与筛选;③采用贝叶斯优化对筛选出的参数进行估值;④方法的适用性评估.同时,将该方法应用于云南异龙湖的三维水动力-水质模型的参数估值中,发现进行参数估值后模型lg(NSE)均大于0.65,表明模型达到了满意的级别.研究表明,当贝叶斯优化算法的采集函数为EI时,仅需要141次迭代lg(NSE)即可达到0.766,该方法对复杂水质模型的参数估值具有一定的借鉴意义.