利用RAPD标记分析卧龙自然保护区不同海拔沙棘种群的遗传变异

从1800m到3400m五个海拔连续取样,用RAPD分子标记研究了卧龙自然保护区中国沙棘种群的遗传结构和遗传变异.用11条寡核苷酸引物,扩增得到151个重复性好的位点,其中143个多态位点,多态率达94.7%.在5个沙棘种群中,总遗传多样性值(HT)为0.289,B种群内的遗传多样性值为0.315,这完全符合沙棘这种多年生、远交的木本植物高遗传变异的特性.5个种群内遗传多样性随海拔升高呈低—高—低变异趋势,在2200m海拔处的B种群遗传多样性达最大值0.315,3400m海拔处的E种群则表现最小,仅0.098.5个种群间的遗传分化值GST=0.406,也即是说有40.6%的遗传变异存在于种群间,59.4%存在种群内.1800m海拔处的A种群与其他种群的明显分离是造成种群间遗传分化大的原因.UPGMA聚类图和PCoA散点图分别进一步确证了5个种群间关系和所有个体间的关系.最后,经过Mantel检测,遗传距离与海拔表现了明显的相关性(r=0.646,P=0.011).     

Estimating Population Size with Noninvasive Capture-Mark-Recapture Data

Abstract:  Estimating population size of elusive and rare species is challenging. The difficulties in catching such species has triggered the use of samples collected noninvasively, such as feces or hair, from which genetic analysis yields data similar to capture-mark-recapture (CMR) data. There are, however, two differences between classical CMR and noninvasive CMR. First, capture and recapture data are gathered over multiple sampling sessions in classical CMR, whereas in noninvasive CMR they can be obtained from a single sampling session. Second, because of genotyping errors and unlike classical CMR, there is no simple relationship between (genetic) marks and individuals in noninvasive CMR. We evaluated, through simulations, the reliability of population size estimates based on noninvasive CMR. For equal sampling efforts, we compared estimates of population size N obtained from accumulation curves, a maximum likelihood, and a Bayesian estimator. For a closed population and without sampling heterogeneity, estimates obtained from noninvasive CMR were as reliable as estimates from classical CMR. The sampling structure (single or multiple session) did not alter the results, the Bayesian estimator in the case of a single sampling session presented the best compromise between low mean squared error and a 95% confidence interval encompassing the parametric value of N in most simulations. Finally, when suitable field and lab protocols were used, genotyping errors did not substantially bias population size estimates (bias < 3.5% in all simulations). The ability to reliably estimate population size from noninvasive samples taken during a single session offers a new and useful technique for the management and conservation of elusive and rare species.     

A spatial zero-inflated poisson regression model for oak regeneration

Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess zeros can occur in regions outside the range of the distribution of a given species. A zero-inflated Poisson regression model is developed, under which the species range is determined by a spatial probit model, including physical variables as covariates. Within that range, species counts are independently drawn from a Poisson distribution whose mean depends on biotic variables. Bayesian inference for this model is illustrated using data on oak seedling counts. Received: May 2004 / Revised: December 2004     

Design of a water quality monitoring network in a large river system using the genetic algorithm

Despite several decades of operations and the increasing importance of water quality monitoring networks, the authorities still rely on experiential insights and subjective judgments in siting water quality monitoring stations. This study proposes an integrated technique which uses a genetic algorithm (GA) and a geographic information system (GIS) for the design of an effective water quality monitoring network in a large river system. In order to develop a design scheme, planning objectives were identified for water quality monitoring networks and corresponding fitness functions were defined using linear combinations of five selection criteria that are critical for developing a monitoring system. The criteria include the representativeness of a river system, compliance with water quality standards, supervision of water use, surveillance of pollution sources and examination of water quality changes. The fitness levels were obtained through a series of calculations of the fitness functions using GIS data. A sensitivity analysis was performed for major parameters such as the numbers of generations, population sizes and probability of crossover and mutation, in order to determine a good fitness level and convergence for optimum solutions. The proposed methodology was applied to the design of water quality monitoring networks in the Nakdong River system, in Korea. The results showed that only 35 out of 110 stations currently in operation coincide with those in the new network design, therefore indicating that the effectiveness of the current monitoring network should be carefully re-examined. From this study, it was concluded that the proposed methodology could be a useful decision support tool for the optimized design of water quality monitoring networks.     

根癌农杆菌介导工业化产甘油假丝酵母的遗传转化

通过构建质粒pCAM3300-zeocin,将其电击转化到根癌农杆菌LBA4404中.将含有目的质粒pCAM3300-zeocin的根癌农杆菌和工业化产甘油假丝酵母(Candida glycerinogenes)WL2002-5共培养,利用zeocin抗性基因为筛选标记,实现了pCAM3300-zeocin对产甘油假丝酵母的转化.并根据产甘油假丝酵母的生长特性,对转化条件进行了优化,在共培养时间为24h,产甘油假丝酵母和根癌农杆菌的细胞比例为1∶(500～1000)时最高转化率达到2个转化子/104个酵母细胞.初步建立了根癌农杆菌介导转化产甘油假丝酵母的转化方法.     

根瘤菌的遗传多样性与系统发育研究进展

根瘤菌 (ｒｈｉｚｏｂｉａ)是一类广泛分布于土壤中的革兰氏阴性细菌。它可以侵染豆科植物根部或茎部形成根瘤和少数茎瘤 ,固定空气中的分子态氮形成氨 ,为植物提供氮素营养 .据估计 ,全球每年生物固氮量达 1.75× 10 8ｔ,为世界工业氮肥产量的4 .37倍[1] .根瘤菌与豆科植物的共生固氮作用是生物固氮中固氮效率最高的体系 .因此 ,根瘤菌与豆科植物的共生固氮作用是实现农业可持续发展的重大研究课题之一 .研究根瘤菌的遗传多样性 ,对于发掘新的根瘤菌种质资源 ,保藏并合理利用根瘤菌基因资源库 ,选育高效菌株直接应用于农业生产有重要意义 .…     

基于遗传算法的大庆市黎明河环境需水量优化配置

为克服水环境管理领域中多目标优化模型加权法求解时主观性强的缺点,将遗传算法与河流水质模拟模型相耦合用于大庆市黎明河生态环境水量优化配置的研究。该模型充分利用遗传算法的开放性和全局搜索的能力,从而得到了较满意的调水方案。对调水方案实施后的河流水质状况进行仿真的结果表明,水质有明显的改善,从而进一步证实了遗传算法应用于城市内河的生态环境水量优化配置的可行性和有效性。     

灰色系统模型GM（1,1）的参数估计方法

提出了灰色系统模型ＧＭ（１,１）参数优化估计的一种新方法－－加速遗传算法,分析了该算法控制参数的优化特性,总结了这些参数的简便设置技术,并给出了它的几个应用实例,该方法也可用于其它环境工程优化计算中。     

Ward法在大气监测网络优化设计中的应用

在大气监测统计资料基础上,进行Ward法聚类分析,从而对宁波市的大气监测点进行优化,得到监测点位的最佳位置分布,通过检验,证明其技术路线是可行的.     

Genetic Algorithm Usage in Water Quality Monitoring Networks Optimization in Gediz (Turkey) River Basin

Selection procedure of the optimum station combination for decreasing the station number from 33 to 14 in water quality monitoring network of Gediz river basin was applied using an optimization method. Gediz basin is one of the important basins and it covers 2.3% of the total surface area of Turkey. The technique includes two stages as the data preparation and the optimization. In the data preparation stage, firstly, alternative station combinations decreased and then station combination scores obtained from assigned selection criteria for point and nonpoint pollution management objectives. Finally, genetic algorithm applied to select the best combination. The results were compared with a prior solution that used dynamic programming as the optimization technique.