Implications of network particle tracking (NPT) for ecological model interpretation

Network particle tracking (NPT), building on the foundation of network environ analysis (NEA), is a new development in the definition of coherence relations within and between connected systems. This paper evaluates three ecosystem models in a comparison of throughflow- and storage-based NEA and NPT. Compartments in models with high indirect effects and Finn cycling showed low correlation of NEA storage and throughflow with particle repeat visits and numbers of particles in compartments at steady state. Conversely, the correlation between NEA and NPT results was high with two models having lower indirect effects and Finn cycling. Analysis of ecological orientors associated with NEA showed NPT to fully support conventional NEA results when the common conditions of donor control and steady state are satisfied. Particle trajectories are recorded in the new concept of a particle “passport”. Ability to track and record particle in-system histories enables views of multiple scales and opens the possibility of making pathway-dependent modeling decisions. NPT may also enable modeling of time, allowing integration of Newtonian, organismal and stochastic modeling perspectives in a single comprehensive analysis.     

Prediction of response of zooplankton biomass to climatic and oceanic changes

This paper examines the long-term variation in zooplankton biomass in response to climatic and oceanic changes, using a neural network as a nonlinear multivariate analysis method. Zooplankton data collected from 1951 to 1990 off the shore of northeastern Japan were analyzed. We considered patterns of the Kuroshio and the Oyashio, sea surface temperature, and meteorological parameters as environmental factors that affect zooplankton biomass. Back propagation neural networks were trained to generate mapping functions between environmental variables and zooplankton biomass. The performance of the network models was tested by varying the numbers of input and hidden units. Changes in zooplankton biomass could be predicted from environmental conditions. The neural network yielded predictions with smaller errors than those of predictions determined by linear multiple regression. The sensitivity analysis of networks was used to extract predictive knowledge. The air pressure, sea surface temperature, and some indices of atmospheric circulation were the primary factors for predictions. The patterns of the Kuroshio and the Oyashio demonstrated different effects among sea areas.     

Evaluating the Predicted Local Extinction of a Once-Common Mouse

Abstract:  In an earlier paper ( Pergams & Nyberg 2001 ) we found that the proportion of the prairie deer mouse ( Peromyscus maniculatus bairdii ), among all local Peromyscus museum specimens collected in the Chicago region, had significantly declined over time. This proportion changed from about 50% before 1900 to <10% in the last 25 years. Based on this proportion a regression model predicted the local extinction of the prairie deer mouse in 2009. To evaluate that prediction, we estimated current deer mouse abundance by live trapping small mammals at 15 preserves in Cook and Lake counties, Illinois (USA) at which prairie deer mice had previously been caught or that still contained their preferred open habitat. In 1900 trap nights, 477 mammals were caught, including 251 white-footed mice ( P. leucopus ), but only one prairie deer mouse. The observed proportion of Peromyscus that were prairie deer mice, 0.4%, was even lower than the 4.5% predicted for 2000. Here we also introduce a simple, new community proportions model, which for any given geographic region compares the proportions of species recently caught with the proportions of species in museums. We compared proportions of seven species collected in Cook and Lake counties and examined by Hoffmeister (1989) with proportions of these species that we caught. Ten percent of the museum community was prairie deer mice, but only 0.2% of our catch was. The current local scarcity of the prairie deer mouse is consistent with the regression-based prediction of its eminent local extinction. More conservation attention should be paid to changes in relative abundance of once-common species.     

施肥与地膜覆盖对土壤有机质平衡的影响

用田间长期定位试验法研究了施肥与地膜覆盖条件下玉米连作在壤质棕壤中有机质的矿化、积累和平衡。结果表明，土壤有机质矿化率在０．０１０７～０．０４３８／ａ之间，施肥与地膜覆盖有利于土壤有机质的积累与平衡，并加快有机质矿化；在本试验各施肥处理中土壤有机质均有所积累；在连年稳定施肥条件下，有机肥的施用会明显提高土壤有机质平衡值     

基于综合评判模型的煤与瓦斯突出危险程度预测

针对某些矿井出现的"低指标"突出现象,提出一种新的综合评判模型,用于煤与瓦斯突出危险程度预测。选取鉴定煤与瓦斯突出的4个单项指标临界值作为参考点,建立煤与瓦斯突出危险性预测综合评判准则。当评判距离Ri属于[0,1],判断突出强度;当Ri属于[-1,0],判断突出危险可能性,量化了突出强度及可能突出的危险程度,并划分突出强度、突出危险可能性等级。利用模型对淮南矿突出程度进行预测,预测结果与实际吻合,实现了定性语言与定量数据相结合的突出预测,为煤与瓦斯突出提供了一种新的预测方法。     

基于马尔科夫灰色残差GM(1,1)模型的火灾损失预测

科学地预测火灾事故及其损失,对于制定火灾防控措施具有重要的现实意义。应用基于马尔科夫的灰色残差GM(1,1)模型,对2002—2011年全国火灾损失进行模拟和预测,结果表明,该模型预测精度明显高于GM(1,1)模型,为火灾损失的预测提供了一种简单而可靠的新途径。     

种间关系预测(ICE)模型在水质基准研究中的应用

水质基准研究主要是基于物种的实验室测试的毒性数据开展的。对于一些毒性数据相对缺乏的化学品,水质基准研究就会受到一定的影响。本文从水质基准研究方法的角度,阐述了种间关系预测(ICE)的基本原理和基本方法,系统介绍了ICE模型的毒性预测方法在水质基准研究中的应用,并通过锌的ICE案例研究证明了模型在中国的可利用性。同时,对ICE模型的不确定性和适用性进行了分析。最后,对ICE模型存在的问题和未来的发展方向进行了探索和展望。     

铁路水害特征与致灾概率预测模型研究

我国铁路水害频发且损失巨大,如何有效防范,已成业界难题,更为运营部门汛期工作重点。但铁路水害泛指由降水或其他水源造成断道、限速事故的灾害,预测预报缺乏针对性。鉴于此,首先,借鉴水利学水害概念,丰富了铁路水害的机理内涵,再结合铁路调研,明确了预测目标的外延;其次,基于京广线湖(南)广(东)区段水害气象、地质与设施条件统计,分析了铁路水害的时空分布特征,建立了致灾因子指标体系;最后,针对降水这一关键诱发因子,筛选出1 h与日降雨量两个相关性最强的计算参数,建立了logistic概率预测模型。模型检验表明:其统计学检验通过,且对于灾害实际情况预测效果较好,可为铁路水害风险管理及防范措施的制定提供参考。     

Cadna/A噪声预测软件在火电厂工程环评中的应用

火电厂较一般的工业项目源强较多,以某35万千瓦超临界间接空冷火电厂为例,采用Cadna/A噪声预测软件预测了投产后产生的噪声影响,预测结果表明火电厂企业采取相应的降噪措施后,厂界噪声能够满足《工业企业厂界环境噪声排放标准》(GB12348-2008)中的3类标准要求,对环境敏感点昼夜噪声预测值能满足《声环境质量标准》(GB3096-2008)的2类标准要求,说明该项目的降噪设计可行。