Inferring extinction of mammals from sighting records, threats, and biological traits

For species with five or more sightings, quantitative techniques exist to test whether a species is extinct on the basis of distribution of sightings. However, 70% of purportedly extinct mammals are known from fewer than five sightings, and such models do not include some important indicators of the likelihood of extinction such as threats, biological traits, search effort, and demography. Previously, we developed a quantitative method that we based on species' traits in which we used Cox proportional hazards regression to calculate the probability of rediscovery of species regarded as extinct. Here, we used two versions of the Cox regression model to determine the probability of extinction in purportedly extinct mammals and compared the results of these two models with those of stationary Poisson, nonparametric, and Weibull sighting-distribution models. For mammals with five or more sightings, the stationary Poisson model categorized all but two critically endangered (flagged as possibly extinct) species in our data set as extinct, and results with this model were consistent with current categories of the International Union for the Conservation of Nature. The scores of probability of rediscovery for individual species in one version of our Cox regression model were correlated with scores assigned by the stationary Poisson model. Thus, we used this Cox regression model to determine the probability of extinction of mammals with sparse records. On the basis of the Cox regression model, the most likely mammals to be rediscovered were the Montane monkey-faced bat (Pteralopex pulchra), Armenian myotis (Myotis hajastanicus), Alcorn's pocket gopher (Pappogeomys alcorni), and Wimmer's shrew (Crocidura wimmeri). The Cox model categorized two species that have recently disappeared as extinct: the baiji (Lipotes vexillifer) and the Christmas Island pipistrelle (Pipistrellus murrayi). Our new method can be used to test whether species with few records or recent last-sighting dates are likely to be extinct.     

基于Box Behnken设计的煤导热系数分析与预测

为研究分析水分、固定碳及密度3个因素对煤导热系数的影响,选取多个矿井不同煤质的煤作为实验煤样,使用NETZSCH LFA457型激光导热系数测试仪进行测定,并引入二次响应曲面试验设计思想,采用Design Expert软件进行煤质指标的Box Behnken试验设计,按设计表筛选实验数据,构建了导热系数多因素、多水平影响下二次回归响应曲面模型,并对不同采样地点的煤导热系数进行了分析与预测。结果表明:二次回归方程P值小于0.000 1,极显著,失拟项为0.072 4,不显著,回归方程具有统计学意义;当水分在0.9%~10.9%,固定碳在49.3%~83.6%,密度在1.2~1.92 g·cm³之间,对煤的导热系数一次项影响程度为密度>固定碳>水分;二次项影响程度为固定碳和密度>水分和密度>水分和固定碳,且固定碳和密度之间存在交互作用,水分和密度、水分和固定碳之间无交互作用;应用响应曲面模型预测的煤导热系数误差为4.3%,满足精度要求。     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

2种除草剂联合胁迫下日本青鳉的逐级行为响应(The Stepwise Behavioral Responses of the Japanese Medaka under the Joint Stress of Two Herbicides)

探讨了日本青鳉在两种除草剂阿特拉津和百草枯联合暴露下的逐级行为响应,采用水质安全在线生物预警系统(BEWs)记录行为强度数据,分析不同暴露浓度、不同暴露时间日本青鳉的行为响应.10、5、1和0.1TU的暴露浓度下行为反应时间分别为:0.74、7.7、29.4和42.2h,而每个浓度不同配比之间行为反应时间差异明显.结果表明:在两种除草剂的暴露下,日本青鳉的逐级行为响应既受化合物浓度高低的影响,又受暴露时间的影响,而且每个浓度两种药物不同配比暴露下的青鳉鱼的逐级行为响应基本一致,每个浓度不同配比之间行为反应时间差异明显,即两种作用机制不同的除草剂对日本青鳉的行为毒性是协同作用.     

基于GIS的矿井瓦斯赋存规律多影响因素智能分析技术

为了提高矿井瓦斯赋存规律分析的准确性和智能化程度,提出了多因素共同作用下瓦斯原始数据筛选和瓦斯赋存规律分析的一种智能化技术。介绍了如何利用地理信息（GIS）技术建立瓦斯地质空间数据模型,结合计算机编程实现了对大量复杂的原始数据的智能分析、处理和筛选。并实现了瓦斯赋存规律多种主控因素情况下多元回归的自动智能化分析,进而使得瓦斯赋存规律模型可以在取得新的原始数据后进行动态、智能的更新。为煤矿工作人员快速、准确的掌握地矿井瓦斯赋存规律提供了先进的手段和工具。     

基于累积Logistic回归模型对化工园区人群认知风险的研究

随着化工园区重大危险源集中监管的深化,政府和企业在安全生产活动中扮演着比以往更重要的角色.如何降低园区内人群对危险源的敏感性,以便在有谣言时不会伴随更大的混乱;什么样的因素对园区内人群认知风险具有显著相关性,以便政府和企业在安全生产活动中更好地发挥职能.针对上述问题,通过对江西省某化工园区进行问卷调查,所得的数据采用累积logistic模型进行分析,得出对风险认知具有显著性差异的因素.有助于政府及企业管理人员对园区内人群认知风险的了解,建议化工园区以及政府需要进一步宣传安全知识,配套安全设备等,最终降低谣言对生产活动的影响、冲击,稳定社会秩序.     

Air‐Water Temperature Relationships in the Trout Streams of Southeastern Minnesota's Carbonate‐Sandstone Landscape

Carbonate‐sandstone geology in southeastern Minnesota creates a heterogeneous landscape of springs, seeps, and sinkholes that supply groundwater into streams. Air temperatures are effective predictors of water temperature in surface‐water dominated streams. However, no published work investigates the relationship between air and water temperatures in groundwater‐fed streams (GWFS) across watersheds. We used simple linear regressions to examine weekly air‐water temperature relationships for 40 GWFS in southeastern Minnesota. A 40‐stream, composite linear regression model has a slope of 0.38, an intercept of 6.63, and R² of 0.83. The regression models for GWFS have lower slopes and higher intercepts in comparison to surface‐water dominated streams. Regression models for streams with high R² values offer promise for use as predictive tools for future climate conditions. Climate change is expected to alter the thermal regime of groundwater‐fed systems, but will do so at a slower rate than surface‐water dominated systems. A regression model of intercept vs. slope can be used to identify streams for which water temperatures are more meteorologically than groundwater controlled, and thus more vulnerable to climate change. Such relationships can be used to guide restoration vs. management strategies to protect trout streams.     

回归分析方法在环境领域中的应用评述

回归分析是一种处理变量间相关关系的有效数理统计方法,回归分析模型目前已应用于环境领域的多个方面,并在实际应用中证实了其准确性和可行性。基本回归分析方法有一元线性回归、多元线性回归和逐步回归等,通过概述了这几种基本回归分析方法的原理及其在环境领域中的应用现状,评述了其应用效果,并预测了回归分析技术在环境领域中应用的发展方向;对在众多环境科学与工程研究领域中,更好地发挥回归分析的作用具有很好的参考价值。     

基于地统计模型的上海大气污染物多建模方法的比较

地统计模型被广泛应用于环境空气污染物暴露模拟,但不同建模方法及其模拟结果之间的对比研究较少.基于上海2016~2019年55个环境空气监测点位的NO₂和PM_2.5观测数据,以及交通路网、排放源兴趣点和卫星数据等地统计变量,应用偏最小二乘回归（PLS）、监督学习线性回归（SLR）和机器学习随机森林（RF）这3种建模方法创建年暴露模型,并进一步应用普通克里金插值（OK）法分析模型残差,构建复合模型.应用交叉验证对模型的模拟效果进行检验,选取每一种建模方法的最优模型结构（是否应用OK）作为最终模型.结果表明,NO₂模型中表现最好的是RF-OK （R_mse²为0.70~0.82）和PLS-OK模型（R_mse²为0.78~0.84）;PM_2.5模型中PLS模型（R_mse²为0.62~0.71）优于SLR-OK （R_mse²为0.40~0.79）和RF-OK （R_mse²：0.31~0.56）模型.应用3种建模方法对上海1 km网格开展年暴露模拟和对比,NO₂模型间模拟结果的相关性（r为0.82~0.91）高于PM_2.5模拟结果的相关性（r为0.66~0.96）.基于3种模型2019年的模拟结果,评估了上海NO₂和PM_2.5的人群暴露水平.     

Estimating influence of stocking regimes on livestock grazing distributions

Livestock often concentrate grazing in particular regions of landscapes while partly or wholly avoiding other regions. Dispersing livestock from the heavily grazed regions is a central challenge in grazing land management. Position data gathered from GPS-collared livestock hold potential for increasing knowledge of factors driving livestock aggregation patterns, but advances in gathering the data have outpaced advancements in analyzing and learning from it. We fit a hierarchical seemingly unrelated regression (SUR) model to explore how season of stocking and the location where cattle entered a pasture influenced grazing distributions. Stocking alternated between summer on one side of the pasture one year and fall on another side of the pasture the next year for 18 years. Waypoints were recorded on cattle for 50 d each year. We focused our analysis on the pasture's 10 most heavily grazed 4-ha units, because these units were the most prone to negative grazing impacts. Though grazing of the study units was always disproportionately heavy, it was much heavier with the summer than fall stocking regime: Bayesian confidence intervals indicate summer grazing of study units was approximately double the average fall grazing value. This is our core result, and it illustrates the strong effect stocking season or date or both can have on grazing distributions. We fit three additional models to explore the relative importance of stocking season versus location. According to this analysis, stocking season played a role, but stocking location was the main driver. Ostensibly minor factors (e.g. stocking location) can greatly influence livestock distributions.