Using long-term data for a reintroduced population to empirically estimate future consequences of inbreeding

Inbreeding depression is an important long-term threat to reintroduced populations. However, the strength of inbreeding depression is difficult to estimate in wild populations because pedigree data are inevitably incomplete and because good data are needed on survival and reproduction. Predicting future population consequences is especially difficult because this also requires projecting future inbreeding levels and their impacts on long-term population dynamics, which are subject to many uncertainties. We illustrate how such projections can be derived through Bayesian state-space modeling methods based on a 26-year data set for North Island Robins (Petroica longipes) reintroduced to Tiritiri Matangi Island in 1992. We used pedigree data to model increases in the average inbreeding level (F ) over time based on kinship of possible breeding pairs and to estimate empirically N_e/N (effective/census population size). We used multiple imputation to model the unknown components of inbreeding coefficients, which allowed us to estimate effects of inbreeding on survival for all 1458 birds in the data set while modeling density dependence and environmental stochasticity. This modeling indicated that inbreeding reduced juvenile survival (1.83 lethal equivalents [SE 0.81]) and may have reduced subsequent adult survival (0.44 lethal equivalents [0.81]) but had no apparent effect on numbers of fledglings produced. Average inbreeding level increased to 0.10 (SE 0.001) as the population grew from 33 (0.3) to 160 (6) individuals over the 25 years, giving a ratio of 0.56 (0.01). Based on a model that also incorporated habitat regeneration, the population was projected to reach a maximum of 331–1144 birds (median 726) in 2130, then to begin a slow decline. Without inbreeding, the population would be expected stabilize at 887–1465 birds (median 1131). Such analysis, therefore, makes it possible to empirically derive the information needed for rational decisions about inbreeding management while accounting for multiple sources of uncertainty.     

Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.     

Synthesis of TiO2 nanoparticles in different thermal conditions and modeling its photocatalytic activity with artificial neural network

Titanium dioxide (TiO₂) nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitrophenol (4-NP). All catalysts were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM). An artificial neural network model (ANN) was developed to predict the photocatalytic removal of 4-NP in the presence of TiO₂ nanoparticles prepared under desired conditions. The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.     

重金属铜在砂质壤土土柱中运移的数值模拟

通过室内土柱出流实验,分析了重金属离子铜在饱和砂质壤土中的运移行为,发现高浓度的入流液有利于铜离子的运移。同时借助于软件包CXTFIT,用确定性平衡模型和非平衡两区模型对铜离子的出流动态进行了拟合,并预测了土柱不同深度处铜离子浓度随时间的变化过程。结果表明,室内饱和均质条件下,应用确定性平衡模型拟合参数模拟精度要高于非平衡两区模型,可以不用考虑不动水体对铜离子运移的影响。     

An air quality forecasting system in Beijing - Application to the study of dust storm events in China in May 2008

An air pollution forecast system, ARIA Regional, was implemented in 2007-2008 at the Beijing Municipality Environmental Monitoring Center, providing daily forecast of main pollutant concentrations. The chemistry-transport model CHIMERE was coupled with the dust emission model MB95 for restituting dust storm events in springtime so as to improve forecast results. Dust storm events were sporadic but could be extremely intense and then control air quality indexes close to the source areas but also far in the Beijing area. A dust episode having occurred at the end of May 2008 was analyzed in this article, and its impact of particulate matter on the Chinese air pollution index (API) was evaluated. Following our estimation, about 23 Tg of dust were emitted from source areas in Mongolia and in the Inner Mongolia of China, transporting towards southeast. This episode of dust storm influenced a large part of North China and East China, and also South Korea. The model result was then evaluated using satellite observations and in situ data. The simulated daily concentrations of total suspended particulate at 6:00 UTC had a similar spatial pattern with respect to OMI satellite aerosol index. Temporal evolution of dust plume was evaluated by comparing dust aerosol optical depth (AOD) calculated from the simulations with AOD derived from MODIS satellite products. Finally, the comparison of reported Chinese API in Beijing with API calculated from the simulation including dust emissions had showed the significant improvement of the model results taking into account mineral dust correctly.     

Air quality management in China: Issues, challenges, and options

This article analyzed the control progress and current status of air quality,identified the major air pollution issues and challenges in future,proposed the long-term air pollution control targets,and ...     

北京城区夏季O3化学生成过程

选取2007年7月1日—8月31日中的21个晴空日,利用观测资料和光化学箱模式计算了北京城区测点的O₃生成速率G(O₃)和O₃生成效率OPE.结果表明,21个晴空日中G(O₃)日最高小时值分布在(18~82)×10^-9h^-1之间;在O₃污染和非污染日G(O₃)最高值的平均水平无显著差异,且与O_x浓度之间不存在一致的对应关系,表明O₃化学生成过程不能全面解释地面O₃浓度的累积,物理传输过程对测点O₃实测浓度有显著作用;各个化学过程对G(O₃)的贡献率对比结果显示,HO₂ 在 NO向NO₂的转化中贡献最大;OPE值分布在2.8~5.8之间,总体水平为4.1±0.1;OPE值与NO_x浓度之间为非线性关系,OPE值随NO_x浓度的增加而减少,表明消减测点附近VOCs排放能有效降低O₃浓度.     

Seismic fragility analysis of a coupled tank-piping system based on artificial ground motions and surrogate modeling

The catastrophic consequences of recent NaTech events triggered by earthquakes highlighted the inadequacy of standard approaches to seismic risk assessment of chemical process plants. To date, the risk assessment of such facilities mainly relies on historical data and focuses on uncoupled process components. As a consequence, the dynamic interaction between process equipment is neglected. In response to this gap, researchers started a progressive integration of the Pacific Earthquake Engineering Research Center (PEER) Performance-Based Earthquake Engineering (PBEE) risk assessment framework. However, a few limitations still prevent a systematic implementation of this framework to chemical process plants. The most significant are: (i) the computational cost of system-level simulations accounting for coupling between process equipment; (ii) the experimental cost for component-level model validation; (iii) a reduced number of hazard-consistent site-specific ground motion records for time history analyses.In response to these challenges, this paper proposes a recently developed uncertainty quantification-based framework to perform seismic fragility assessments of chemical process plants. The framework employs three key elements: (i) a stochastic ground-motion model to supplement scarcity of real records; (ii) surrogate modeling to reduce the computational cost of system-level simulations; (iii) a component-level model validation based on cost-effective hybrid simulation tests. In order to demonstrate the potential of the framework, two fragility functions are computed for a pipe elbow of a coupled tank-piping system.     

陕西省山地日照时间的空间分布特征

基于100 m×100 m分辨率的DEM数据,根据太阳光线与起伏地形之间的几何关系,全面考虑坡度、坡向、地形遮蔽等地形因子对可照时间的影响,通过数值模拟,确定山区可照时间计算模型;同时利用常规气象站资料考虑大气对日照时间的影响,建立陕西省山区日照时间估算模型。采用交叉验证和个例年分析法进行模型验证,模型的精度和稳定性均较好。通过模型计算陕西省100 m×100 m分辨率各月可照时间、气候平均日照时间的空间分布。结果表明:局地地形对日照时间空间分布影响比较明显,山区可照时间与同纬度平地相比明显差异,表现出可照时间的非地带性分布特征;尤以冬季地形作用最显著;夏季影响最弱;同时年日照时间呈现出北高南低的空间分布特征,但局地地形因子的影响依然存在。最后详细探讨了坡度、坡向、地形遮蔽等因子对山区日照时间的局地影响规律。     

基于农户行为的土地利用人工社会模型的构造与应用

为探讨人工社会在土地利用变化研究领域中的应用,研究创建了一个模拟土地利用变化过程的人工社会模型(LUC-ASM)。该模型以农户和农民为两类主体(agents),模拟分析不同社会经济条件和政策影响下,两类主体对自己承包土地利用方式的微观决策所可能产生的宏观效应及其时空变化特征。以鄱阳湖区一个现实村落为主要研究对象,以农户从事各类生产活动的行为特征为准则,利用LUC-ASM模型模拟未来30年内该村落土地利用变化过程。结果表明,未来这一地区土地利用的变化与人口增长结构调整方式、国家土地利用与环保政策、社会经济发展态势等因素密切相关;在城市化的影响下,该地区的农业劳动力将会出现短缺,农村土地有可能被撂荒,需要及早采用政策措施予以避免。总体而言,LUC-ASM模型能够形象地反映土地利用变化过程的微观驱动机理及各种社会经济与环境因素影响的宏观效应,有助于深化我们对人与自然环境相互作用的认识。