模拟酸雨对珍稀濒危植物红豆杉幼苗的影响研究

利用模拟酸雨实验对珍稀濒危植物曼地亚红豆杉和南方红豆杉生长及生理生化指标进行研究。结果表明,pH4.5的模拟酸雨促进了曼地亚红豆杉(Taxus media)和南方红豆杉(T.mairei)的株高生长(与对照相比分别促进了19%和72%),pH3.5的模拟酸雨对其有一定的影响,但不显著。pH4.5和pH3.5的酸雨处理下,曼地亚红豆杉基径比对照分别下降了46%和42%;南方红豆杉的相应值分别是34%和43%。总的来说,模拟酸雨促进了这两种红豆杉叶绿素的质量分数,类胡萝卜素的质量分数变化因种而异。酸雨处理提高了曼地亚红豆杉抗氧化系统酶活性,与对照相比约提高了5%～142%;南方红豆杉的相应值为-16%～35%。曼地亚红豆杉游离脯铵酸和丙二醛(MDA)的质量分数比对照提高了13%～73%;而南方红豆杉的相应质量分数基本等于或低于对照。与南方红豆杉相比,曼地亚红豆杉对酸雨更加敏感。     

红豆杉培养细胞紫杉醇的分离及鉴定

红豆杉培养细胞紫杉醇的分离及鉴定甘烦远，郑光植，彭丽萍，沈月毛（中国科学院昆明植物研究所昆明６５０２０４）关键词红豆杉；紫杉醇；细胞培养；分离鉴定ＩＳＯＬＡＴＩＯＮＡＮＤＩＤＥＮＴＩＦＩＣＡＴＩＯＮＯＦＴＡＸＯＬＦＲＯＭＣＵＬＴＵＲＥＤＣＥＬＬＳＯＦ...     

红豆杉细胞抗病反应的诱导及其与紫杉醇合成的关系

采用从红豆杉植株不同部位（叶、内皮、根等）分离筛选获得的各种真菌制备的激发子，对6种不同红豆杉细胞株进行诱导作用研究。结果表明，不同细胞对不同真菌制备的激发子的抗病反应有显著的区别，并建立3种-具菌互作模式：极不相容（细胞抗性强，受损小），不相容（抗性强，受损也较严重），相容（细胞抗性小，受损严重）。结果还表明，极不相容和不相容互作模式中细胞的紫杉醇产量显著高于CK，而相容互作模式的细胞不合有紫杉, 同一真菌由不同的方法制备成的不同分子量大小或不同成分的激发子，对细胞的抗病反应与紫杉醇合成的影响不同,这些结果为诱导作用机理及调控红豆杉细胞合成紫杉醇的研究具有指导意义。     

欧洲红豆杉细胞培养的研究

从欧洲红豆杉（Ｔａｘｕｓｂａｃｃａｔａ）的嫩茎及什叶诱导出愈伤组织，并对之进行了愈伤组织培养及细胞悬浮培养研究；利用ＨＰＬＣ方法测定了培养物合成紫杉醇的能力．探索了提高培养细胞生长率及紫杉醇含量的一些措施．结果表明，欧洲红豆杉愈伤组织及悬浮培养细胞的生长速率已分别达到０．２７ｇ·Ｌ－１·ｄ－１和０．３５ｇ·Ｌ－１·ｄ－１，紫杉醇含量分别为０．００３１％和０．０１７％。     

云南红豆杉的保护和开发利用

云南红豆杉(Taxus yunnanensis W.C.Cheng&L.K.Fu)是红豆杉属植物中紫杉醇含量最高的树种,同时也是我国红豆杉属中分布最广和资源蕴藏量最丰富的物种.然而,近年来由于人为大量砍伐,云南红豆杉野生资源遭到严重破坏,加上生长缓慢且天然更新困难,目前处于濒危状态.对云南红豆杉天然资源的保护和药用人工...     

微量元素对红豆杉菌根菌生长的影响

微量元素对植物的生长发育有着十分重要的意义。对太行山野生红豆杉(Taxus chinensis var.mairei)菌根菌菌丝在分别加有硫酸锌、钼酸铵以及硫酸亚铁的PDA培养基中的生长发育状况进行了试验研究。所得数据同PDA对照组进行比较分析后,结果表明:Mo元素对红豆杉菌根菌菌丝的生长发育有很好的促进作用;Fe元素明显地促进红豆杉菌根菌菌丝的生长发育;Zn元素在一定范围内对红豆杉菌根菌菌丝的生长发育有一定的抑制作用。实验结果揭示出红豆杉菌根菌生长发育的一些营养因素。     

南方红豆杉种子的化学成分分析

对南方红豆杉种子 (种仁 )的化学成分分析表明 :南方红豆杉干重 (DW)种子粗蛋白、粗脂肪、淀粉、可溶性糖的质量分数wDW分别为 9.92g (10 0g) -1,72 .86g (10 0g) -1,4 .15g (10 0g) -1,0 .38g (10 0g) -1;氨基酸的wDW为 9.10g (10 0g) -1,其中必需氨基酸占总氨基酸量的 2 8.0 5 % ;种子油脂肪酸组成以不饱和的油酸和亚油酸为主 ,其相对含量为 4 8.4 %和 4 2 .2 % ;种子元素质量分数分别为 :wDW(N) =15 87.2mg (10 0g) -1,wDW(P) =5 6 5 .4mg (10 0g) -1,wDW(K) =2 6 1.1mg(10 0g) -1,wDW(Ca) =18.4mg (10 0g) -1,wDW(Mg) =35 0 .5mg (10 0g) -1,其余微量元素含量由多到少依次为Zn >Fe>Na>Cu >Mn .表 4参 6     

紫杉醇合成代谢途径中苯丙基转移酶cDNA的克隆

从南方红豆杉(Taxus chinensis var．mairei)愈伤组织中直接提取出总RNA．采用RT—PCR技术获得苯丙基转移酶基因cDNA片段，将该片段克隆在载体pGEM—T Easy Vector上并转化到大肠杆菌DH5α中，经Spe I／Xho I双酶切检测及cDNA全长序列分析，证实该片段确为苯丙基转移酶基因，与已报道的从东北红豆杉(Taxus cuspidata)中得到的苯丙基转移酶基因序列具很高的同源性．图5参6     

中国红豆杉内生放线菌En-1的分离、鉴定及其次生代谢物的研究

采用选择性分离方法对药用植物红豆杉Taxus chinensis嫩枝和叶进行内生菌分离,得到一株放线菌En-1,经鉴定为链霉菌Streptomyces sp.;并对该菌进行液体培养条件优化、次生代谢物初筛及其抗菌活性的研究。通过添加宿主植物浸出物至En-1液体培养中以考察宿主对后者生长的影响,结果表明红豆杉叶浸出物能促进En-1的体外生长;而且En-1次生代谢物对5株供试菌中的黑曲霉具有抑制活性。通过En-1菌培养基优化并联合波谱法检测其发酵产物,显示8#配方（淀粉25 g.L-1,KNO3 1 g.L-1,K2HPO4.3H2O 0.5 g.L-1,NaCl 0.5 g.L-1,FeSO4.7H2O 0.01 g.L-1,MgSO4.7H2O 0.5 g.L-1）培养液中,En-1所产的次生代谢物最具结构多样性;同时8#配方也有利于该菌产生抗菌活性产物。本研究提示红豆杉内生放线菌可作为寻求药物先导化合物的资源菌。     

基于最大熵模型模拟气候变化下中国两个沼泽藓类属的潜在分布

皱蒴藓属(Aulacomnium)和寒藓属(Meesia)是北温带沼泽或湿原藓类属的代表.沼泽湿地对气候变暖较为敏感,对于维持区域水循环和水文功能具有重要影响.基于中国皱蒴藓属4种52个和寒藓属3种20个地理分布点及19个环境变量,利用最大熵模型和Arc GIS 10.2软件,分别预测两个属在中国的适生分布区以及未来气候情景下物种潜在地理分布的变化.结果显示:(1)皱蒴藓属主要分布在中国东北和西北部分地区以及西南部分高山地带;寒藓属主要分布在中国东北大部分地区和四川北部高原.(2)最暖季度平均气温、最干季度降雨量和最干月降雨量是影响皱蒴藓属在中国分布的主要气候因子;年降雨量、最干季度平均气温、温度季节性变动系数和最暖季度平均气温是影响寒藓属在中国分布的主要气候因子.(3)在未来(2061-2080年)气候情景下,皱蒴藓属分布面积将比现代气候下增加5.94%,而寒藓属分布面积将减少0.27%.结果表明气候变化对两个沼泽藓类属的影响不同,可为探讨气候变化下苔藓植物物种分布变化提供参考资料.     

Kriging插值法在植物物种地理分布空间格局研究中的应用

Kriging插值法是一种定量化描述地理空间分布格局的方法,主要应用于空间采样以及相关的一些空间格局分析。而物种的地理空间分布是物种在自然及人类活动共同作用下的结果。物种分布的研究目前主要从气候的角度来探讨其与气候之间的关系,并取得了很好的结果,但是仅从气候考虑又有局限性,而且很难真实地反映出物种地理空间的分布格局。因此,将Kriging插值法引进到物种分布的研究中,并以我国广泛分布的物种——栓皮栎为例,探讨其可行性。结果表明：Kriging插值法能很好地拟合物种地理空间分布的实际情况,其误差程度较小。该研究的结果可为物种资源的开发利用和物种的引种栽培提供理论依据。     

Forecasting New Zealand Mudsnail invasion range: model comparisons using native and invaded ranges.

Evaluations of the potential distribution of invasive species can increase the efficiency of their management by focusing prevention measures. Generally, ecological models are built using occurrence data from a species' native range to predict the distribution in areas that the species may invade. However, historical and geographical constraints can limit a species' native distribution. Genetic Algorithm for Rule-set Production (GARP), an ecological niche modeling program, was used to predict the potential distribution of the invasive, freshwater New Zealand mudsnail, Potamopyrgus antipodarum, in Australia and North America. We compared the strength of the predictions made by models built with data from the snail's native range in New Zealand to models built with data from the locations invaded by the species. A time-series analysis of the Australian models demonstrated that range-of-invasion data can make better predictions about the potential distribution of invasive species than models built with native range data. Large differences among the model forecasts indicate that uncritical choice of the data set used in training the GARP models can result in misleading predictions. The models predict a large expansion in the range of P. antipodarum in both Australia and North America unless prevention measures are implemented rapidly.     

能源植物黄连木在我国的地理分布规律

为了更好地开发利用生物柴油能源植物黄连木（Pistacia chinensis Bunge）,对其在我国的地理分布规律进行了系统调查研究,结果表明：黄连木的地理分布范围为北纬18°09′～40°09′、东经96°52′～123°14′,资源遍布我国华北、华南、西南、华中、华东与西北地区的25个省、自治区、直辖市;分布区地形以高原、山地为主,土壤母岩以石灰岩为主,土壤类型以褐土为主,跨越我国温带、亚热带、热带地区;黄连木的水平分布区主要位于云南潞西—西藏察隅—四川甘孜—青海循化—甘肃天水—陕西富县—山西阳城—河北顺平—北京西山一线以东、以南,整体上呈现连续分布的特征,局部地区有一定的间断分布;从我国西部到东部,其垂直分布的上限与下限均呈现逐渐降低的趋势,从南方到北方,这种降低趋势不太明显;黄连木在我国的资源分布区可以划分为集中分布区、次集中分布区、零星分布区和沿海地带零星分布区四种类型。     

A GIS-Based Comparison of the Mexican National and IUCN Methods for Determining Extinction Risk

Abstract:  The national systems used in the evaluation of extinction risk are often touted as more readily applied and somehow more regionally appropriate than the system of the International Union for Conservation of Nature (IUCN). We compared risk assessments of the Mexican national system (method for evaluation of risk of extinction of wild species [MER]) with the IUCN system for the 16 Polianthes taxa (Agavaceae), a genus of plants with marked variation in distribution sizes. We used a novel combination of herbarium data, geographic information systems (GIS), and species distribution models to provide rapid, repeatable estimates of extinction risk. Our GIS method showed that the MER and the IUCN system use similar data. Our comparison illustrates how the IUCN method can be applied even when all desirable data are not available, and that the MER offers no special regional advantage with respect to the IUCN regional system. Instead, our results coincided, with both systems identifying 14 taxa of conservation concern and the remaining two taxa of low risk, largely because both systems use similar information. An obstacle for the application of the MER is that there are no standards for quantifying the criteria of habitat condition and intrinsic biological vulnerability. If these impossible-to-quantify criteria are left out, what are left are geographical distribution and the impact of human activity, essentially the considerations we were able to assess for the IUCN method. Our method has the advantage of making the IUCN criteria easy to apply, and because each step can be standardized between studies, it ensures greater comparability of extinction risk estimates among taxa.     

The performance of state-of-the-art modelling techniques depends on geographical distribution of species

We explored the effects of prevalence, latitudinal range and clumping (spatial autocorrelation) of species distribution patterns on the predictive accuracy of eight state-of-the-art modelling techniques: Generalized Linear Models (GLMs), Generalized Boosting Method (GBM), Generalized Additive Models (GAMs), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), Mixture Discriminant Analysis (MDA) and Random Forest (RF). One hundred species of Lepidoptera, selected from the Distribution Atlas of European Butterflies, and three climate variables were used to determine the bioclimatic envelope for each butterfly species. The data set consisting of 2620 grid squares 30′ × 60′ in size all over Europe was randomly split into the calibration and the evaluation data sets. The performance of different models was assessed using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were then related to the geographical attributes of the species using GAM. The modelling performance was negatively related to the latitudinal range and prevalence, whereas the effect of spatial autocorrelation on prediction accuracy depended on the modelling technique. These three geographical attributes accounted for 19–61% of the variation in the modelling accuracy. Predictive accuracy of GAM, GLM and MDA was highly influenced by the three geographical attributes, whereas RF, ANN and GBM were moderately, and MARS and CTA only slightly affected. The contrasting effects of geographical distribution of species on predictive performance of different modelling techniques represent one source of uncertainty in species spatial distribution models. This should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

村落生态系统分布特征和模式的探讨

村落生态系统的分布是地表突出和普遍的现象。本文概括总结了区域性村落生态系统分布的一般性原则，其分布特征表现为景观性、分散集聚性、动态迁移性、梯度性、趋适性、界限性以及人文性等各个方面；对诸如平原、山区、湖区、盆地、丘陵台地、岛屿以及过渡区等各种地貌类型区提出相应的理论分布模式。     

气候变化对物种影响研究综述

综述了气候变化对物种的影响,表明气候变化会造成生物物候期的改变,导致物种地理分布的变化,增加物种的灭绝速率。分析了利用模型进行气候变化影响模拟的技术,指出模型的适用性和不确定性。最后,针对中国相关研究的不足,展望了未来开展气候变化影响研究的方向。     

Transferability of Species Distribution Models: a Functional Habitat Approach for Two Regionally Threatened Butterflies

Abstract:  Numerous models for predicting species distribution have been developed for conservation purposes. Most of them make use of environmental data (e.g., climate, topography, land use) at a coarse grid resolution (often kilometres). Such approaches are useful for conservation policy issues including reserve-network selection. The efficiency of predictive models for species distribution is usually tested on the area for which they were developed. Although highly interesting from the point of view of conservation efficiency, transferability of such models to independent areas is still under debate. We tested the transferability of habitat-based predictive distribution models for two regionally threatened butterflies, the green hairstreak ( Callophrys rubi ) and the grayling ( Hipparchia semele ), within and among three nature reserves in northeastern Belgium. We built predictive models based on spatially detailed maps of area-wide distribution and density of ecological resources. We used resources directly related to ecological functions (host plants, nectar sources, shelter, microclimate) rather than environmental surrogate variables. We obtained models that performed well with few resource variables. All models were transferable—although to different degrees—among the independent areas within the same broad geographical region. We argue that habitat models based on essential functional resources could transfer better in space than models that use indirect environmental variables. Because functional variables can easily be interpreted and even be directly affected by terrain managers, these models can be useful tools to guide species-adapted reserve management.     

Probability distributions and statistical inference for axial data

Observations on axes which lack information on the direction of propagation are referred to as axial data. Such data are often encountered in enviromental sciences, e.g. observations on propagations of cracks or on faults in mining walls. Even though such observations are recorded as angles, circular probability models are inappropriate for such data since the constraint that observations lie only in [0, π) needs to be enforced. Probability models for such axial data are argued here to have a general structure stemming from that of wrapping a circular distribution on a semi-circle. In particular, we consider the most popular circular model, the von Mises or circular normal distribution, and derive the corresponding axial normal distribution. Certain properties of this distribution are established. Maximum likelihood estimation of its parameters are shown to be surprisingly, in contrast to trigonometric moment estimation, numerically quite appealing. Finally we illustrate our results by several real life axial data sets. Received: September 2004/ Revised: December 2004     

Updating coarse-scale species distribution models using small fine-scale samples

Large, fine-grained samples are ideal for predictive species distribution models used for management purposes, but such datasets are not available for most species and conducting such surveys is costly. We attempted to overcome this obstacle by updating previously available coarse-grained logistic regression models with small fine-grained samples using a recalibration approach. Recalibration involves re-estimation of the intercept or slope of the linear predictor and may improve calibration (level of agreement between predicted and actual probabilities). If reliable estimates of occurrence likelihood are required (e.g., for species selection in ecological restoration) calibration should be preferred to other model performance measures. This updating approach is not expected to improve discrimination (the ability of the model to rank sites according to species suitability), because the rank order of predictions is not altered. We tested different updating methods and sample sizes with tree distribution data from Spain. Updated models were compared to models fitted using only fine-grained data (refitted models). Updated models performed reasonably well at fine scales and outperformed refitted models with small samples (10-100 occurrences). If a coarse-grained model is available (or could be easily developed) and fine-grained predictions are to be generated from a limited sample size, updating previous models may be a more accurate option than fitting a new model. Our results encourage further studies on model updating in other situations where species distribution models are used under different conditions from their training (e.g., different time periods, different regions).