庞泉沟自然保护区华北落叶松林的自组织特征映射网络分类与排序

自组织特征映射网络（SOM）是新近引入植物生态学的分析方法,对复杂问题和非线性问题具有较强的分析和求解功能。本研究应用SOM分类和排序研究了庞泉沟自然保护区华北落叶松林。研究结果表明,SOM将120个样方分为7个植物群落类型,分类结果具有明确的生态意义;样方和物种在SOM训练图上呈现一定规律的分布;7个群落类型各有其分布范围和界限,揭示了群落间的生态关系。在此基础上,通过引入一种在SOM训练图上可视化环境因子梯度的方法,能够较好地完成样方、物种和环境因子相互关系的分析,揭示了海拔是影响该区华北落叶松林生长和分布的最主要因子。生态分析表明SOM分类和排序是一种有效的梯度分析方法,适用于表征生态特征和探索群落和环境相互关系的研究。     

自组织特征映射网络在北京松山自然保护区山地草甸数量分析中的应用

采用自组织特征映射网络(SOM)对松山自然保护区山地草甸群落进行了数量分类研究, 并用Kruskal-Wallis检验和Tukey多重比较方法分析了草甸类型的环境因子之间差异的显著性。结合完全连接法和SOM, 将松山自然保护区的山地草甸群落分为7个类型, 其群落结构、物种组成等特征明显。这7个山地草甸群落主要受海拔高度、坡度、枯枝落叶层厚度和土壤深度等环境因子的影响, 其差异极显著。生态学分析表明SOM是非常有效的植物群落分类方法, 适合于山地草甸植被的 研究。     

庞泉沟自然保护区植物群落分类学多样性

为了研究庞泉沟自然保护区植物群落分类学多样性及其与环境因子间的关系,收集了33块样地的植物群落数据及样地环境信息,计算了分类差异变异指数(Λ⁺)和平均分类差异指数(Δ⁺),分析了Λ⁺和Δ⁺与环境因子的相关性.结果表明: Λ⁺和Δ⁺的平均值分别为270和76.5;Λ⁺与海拔和坡向均呈显著负相关;Δ⁺与纬度呈显著正相关,与坡向呈显著负相关.从33个样地在置信漏斗的位置来看,庞泉沟自然保护区植物群落有较高的分类学多样性,主要原因是保护区建立30多年来,生态环境和植物资源得到有效保护,人类活动干扰影响轻微.
     

山西庞泉沟自然保护区褐马鸡种群数量特征的研究

本文作者于1982—1988年在山西庞泉沟国家级自然保护区对褐马鸡(Crossoptilon mantchuri-cum)的种群数量进行了调查。结果种群密度为0.077(只/公顷),A、B、C垂直带间的数量分别为0.091、0.093、0.048,各占种群数量的39.22％、40.09％和20.69％,D带经调查,尚未见有分布。在繁殖前的5月为种群数量最低基数(0.030只);繁殖后的7月数量最高(0.099只)。     

模糊ＩＳＯＤＡＴＡ在草地植物群落分类上的应用

 模糊集合理论在元素与集合的关系中引入了连续变化的隶属度,因而能适应植物群落作连续和明显间断分布同时存在的情况。本文运用以模糊集合理论为基础的ISODATA聚类分析方法对松嫩平原草地植物群落进行了数量分类,结果说明模糊ISODATA聚类比传统的分类方法有更为客观、灵活的特点,是一种较为理想的植被数量分类方法。     

安徽省皇藏峪自然保护区森林植被的数量分类

本文用聚类分析的方法,将皇藏岭自然保护区的森林植被划分出1个植被型,2个群系组和7个群系。并分别描述了各群系的主要群落学特征。通过排序揭示出植被沿水分梯度变化、分布的规律。样地在二维空间中集散分布的特点进一步印证了聚合分类的可信性结果。     

庞泉沟自然保护区华北落叶松土壤种子库的研究

采用样地内小样方取样,物理方法分离,室内测量,试验,分析的方法,对庞泉沟国家级自然保护区内,华北落叶松４个林型的林下土壤种子数量,质量及分布进行了研究。分析了影响林下土壤种子数量的因素,提供了华北落叶松土壤种子库的基本背景。     

拉萨河流域植物群落的数量分类与排序

青藏高原植物群落空间分异格局是异质生境条件下物种性状、种间相互作用等生态学过程共同作用的结果,对其分析有助于深入理解群落形成与环境因子之间的关系。基于拉萨河流域自然植被样带调查,采用双向指示种分析(TWINSPAN)和典范对应分析(CCA)等方法,探讨了群落的结构组成及影响其结构分异的主导环境因子。结果表明:(1) TWINSPAN数量分类将拉萨河流域草地系统划分成12个群系类型,即圆叶合头菊+唐古拉翠雀花群系;紫花针茅群系;青藏臺草群系;雪层杜鹃+鲜卑花-西藏嵩草群系;高山嵩草群系;小叶金露梅群系;硬叶柳+杯腺柳群系;水栒子+拱枝绣线菊-高山嵩草群系;绢毛蔷薇-冷蒿+白草群系;大果圆柏-垂穗披碱草群系;铺地柏-藏橐吾+高原荨麻群系;醉鱼草+砂生槐群系。12种群系类型包含了较多的植被类型,包括高寒灌丛草甸、高寒灌丛草原、稀树草原、高寒草甸和高寒草原等。(2) CCA排序表明:影响拉萨河流域植物群系分布的主要环境因子是年均温度、海拔和经度和纬度,其次是年均降雨量。(3) TWINSPAN分类与CCA排序结合反映了群系分布格局变异与环境因子之间的关系,可为拉萨河流域草地的保护和可持续利用,以及相关的植被群落研究提供参考。     

Application of self-organizing neural networks to classification of plant communities in Pangquangou Nature Reserve, North China

Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field. The arti-ficial neural network is a comparatively new tool for data analysis. The self-organizing feature map (SOFM) is powerful tool for clustering analysis. SOFM has been applied to many research fields and it was applied to the classification of plant communities in the Pangquangou Nature Reserve in the present work. Pangquangou Nature Reserve, located at 37°20'-38°20' N, 110°18'-111°18' E, is a part of the Luliang Mountain range. Eighty-nine samples (quadrats) of 10 m x 10 m for for-est, 4 m × 4 m for shrubland and 1m x 1m for grass-land along an elevation gradient, were set up and species data was recorded in each sample. After discussion of the mathematical algorism, clustering technique and the pro-cedure of SOFM, the classification was carried out by using NNTool box in MATLAB (6.5). As a result, the 89 samples were clustered into 13 groups representing 13 types of plant communities. The characteristics of each community were described. The result of SOFM clas-sification was identical to the result of fuzzy c-mean clus-tering and consistent with the distribution patterns of vegetation in the study area and shows significant eco-logical meanings. This suggests that SOFM may clearly describe the ecological relationships between plant com-munities and it is a very effective quantitative technique in plant ecology research.     

Application of self-organizing neural networks to classification of plant communities in Pangquangou Nature Reserve, North China

Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field. The artificial neural network is a comparatively new tool for data analysis. The self-organizing feature map (SOFM) is powerful tool for clustering analysis. SOFM has been applied to many research fields and it was applied to the classification of plant communities in the Pangquangou Nature Reserve in the present work. Pangquangou Nature Reserve, located at 37°20′–38°20′ N, 110°18′–111°18′ E, is a part of the Luliang Mountain range. Eighty-nine samples (quadrats) of 10m × 10m for forest, 4m × 4 m for shrubland and 1m × 1m for grassland along an elevation gradient, were set up and species data was recorded in each sample. After discussion of the mathematical algorism, clustering technique and the procedure of SOFM, the classification was carried out by using NNTool box in MATLAB (6.5). As a result, the 89 samples were clustered into 13 groups representing 13 types of plant communities. The characteristics of each community were described. The result of SOFM classification was identical to the result of fuzzy c-mean clustering and consistent with the distribution patterns of vegetation in the study area and shows significant ecological meanings. This suggests that SOFM may clearly describe the ecological relationships between plant communities and it is a very effective quantitative technique in plant ecology research. __________ Translated from Acta Ecologica Sinica, 2007, 27(3): 1005–1010 [译自: 生态学报]     

Quantitative classification and ordination of forest communities in Pangquangou National Nature Reserve

Quantitative analysis of ecological relationships between vegetation and the environment has become an essential means in the field of research of modern vegetation ecology. In this article, based on data from 84 quadrates, forest communities in this reserve were investigated using TWINSPAN, DCA and DCCA. The results will be helpful in the construction and development of Pangquangou National Nature Reserve. Using TWINSPAN, the forest communities were classified into seven types. The distribution pattern of vegetation reflects the comprehensive influence of environments. The results of DCA and DCCA clearly reflect the relationship between the pattern of forest communities and environmental gradients. The ordination result of DCCA indicates that altitude is more important than other environmental factors because the change of altitude gradient will lead to changes in the temperature and humidity gradients. The first of the DCA ordination axes indicates the humidity gradient, and the second indicates the temperature gradient. All these results show that the main factors restricting the distribution of communities in this reserve are temperature and humidity. The ecological meaning of the ordination axis in DCCA is much clearer than that in DCA, and the species-environment correlation of DCCA is more obvious than DCA. The first DCCA axis indicates the altitude gradient among the communities, while the second is the gradient in aspect and slope among the communities. DCCA ordination can simultaneously express similarities of species and environment. Therefore, the quadrat location in the DCCA ordination figure is much closer than in the DCA.     

庞泉沟自然保护区森林群落物种多样性

以庞泉沟自然保护区海拔1630-2430 m的森林群落为研究对象,采用样方法(共取10 m×10 m样方84个)进行物种组成和环境因子调查,对物种多样性及其与环境因子的关系进行研究.结果表明:(1)随着森林群落垂直分布的变化,即从山体下部(1630～2000 m)的华北落叶松灌木群落到山体中部(2000～2200 m)的华北落叶松云杉桦木混交林群落,再到山体上部(2200～2430 m)的华北落叶松苔草群落,物种丰富度指数(R_0)与Shannon-Wiener指数(H)呈现由小增大(在中部达最大)和由大减小的趋势;而Simpson指数(λ)和Pielou均匀度指数(E)则呈现持续增大趋势.(2)海拔与R_0(r=-0.541,P<0.01)、H(r=-0.377,P<0.01)呈极显著负相关,与λ(r=0.495,P<0.01)呈极显著正相关;坡向与R_0(r=-0.365,P<0.01)呈极显著负相关,与日(r=-0.250,P<0.05)呈显著负相关,与λ(r=0.356,P相似文献   

庞泉沟国家自然保护区森林群落的数量分类和排序

根据84块样方资料,对庞泉沟国家自然保护区的森林群落进行TWINSPAN分类和DCA、DCCA排序.结果表明：（1）TWINSPAN将该区的森林群落分为7个类型;（2）样方与物种的DCA排序及样方的DCCA排序较好地揭示了该区森林群落的分布格局与环境梯度的关系：DCCA第一轴明显地反映出森林群落的海拔梯度,即热量因子,沿DCCA第一轴从左到右,海拔逐渐升高,森林群落对热量的要求降低;DCCA第二轴主要表现出森林群落的坡向、坡度变化趋势,即水分和光照因子,沿DCCA第二轴,从下到上,坡向由阴坡向阳坡变化,坡度由平缓变陡峭.其中海拔梯度是环境因子中对森林群落分布起决定性作用的因子.（3）与DCA相比,DCCA的排序轴更有利于生态意义的解释,后者能同时反映样方间在种类组成上及环境因子组成上的相似性,表现在排序图中样方较集中,群落间的界线变得较模糊,因此如果同分类结合使用,DCA的效果要好于DCCA.（4）TWINSPAN分类与DCA和DCCA排序的结果,同时表明了该地区森林群落的垂直分布格局.     

河北省塞罕坝保护区天然植物群落数量分类

运用双向指示种(TWINSPAN)和除趋势典范对应分析(DCCA)等方法定量分析了河北省塞罕坝保护区天然植物群落分布与环境因子的关系.结果表明: 塞罕坝保护区天然植物群落可划分为8种类型,即地榆+异穗苔草+金莲花群落、地榆+老芒麦+毛蕊老鹳草群落、披针叶苔草+铁丝草群落、柔毛绣线菊-铁杆蒿群落、山杏+柔毛绣线菊-铁杆蒿+披针叶苔草群落、蒙古栎+硕桦-胡枝子+柔毛绣线菊-披针叶苔草群落、白桦+山杨-毛榛+山刺玫-糙苏+披针叶苔草+唐松草群落、云杉+白桦-山刺玫+河北柳-披针叶苔草+节节草群落;DCCA分析表明,坡位和土壤养分是影响塞罕坝保护区天然植物群落空间分布的主要因子.地形、空间和土壤因子解释了物种格局变异的34.02%,其中,土壤因子占15.67%,地形因子占10.05%,空间因子占6.00%.在各类因子间的交互作用(2.30%)中,土壤和地形因子的交互作用最大(0.93%),土壤、地形和空间因子三者的交互作用最小(0.16%).65.98%未能解释的部分,可能归因于未被选取的因子如种间的协同和竞争作用或者人为干扰.     

Main plant communities and characteristics of Kalamaili Ungulate Nature Reserve in east Junggar Basin

The main vegetation types of Kalamaili Nature Reserve in east Junggar Basin were investigated in this paper, and based on the data from 54 plot samples, the characteristics of the main desert plant communities from the main vegetation types were quantitatively described. And the results showed that, the vegetation in this reserve can be divided in to 9 major desert plant community types were recognized: Form. Haloxylon ammodendron, Form. Krascheninnikovia ceratoides, Form. Atraphaxis spinosa, Form. Reaumuria songarica, Form. Artemisia songarica, Form. Convolvulus tragacanthoides, Form. Anabasis salsa, Form. Stipa glareosa and Form. Seriphidium terrae-albae, and then, 16 associations were divided from these 9 major community types.     

准噶尔盆地东部卡拉麦里山有蹄类自然保护区主要植物群落及其特征

该文调查了准噶尔盆地东部卡拉麦里山自然保护区的主要植被类型, 通过对54个样地的样方数据分析, 量化描述了这一保护区的主要植物群落特征。结果表明, 这一地区植被主要分为梭梭群系(Form. Haloxylon ammodendron)、驼绒藜群系(Form. Krascheninnikovia ceratoides)、刺木蓼群系(Form. Atraphaxis spinosa)、红砂群系(Form. Reaumuria songarica)、准噶尔沙蒿群系(Form. Artemisia songarica)、刺旋花群系(Form. Convolvulus tragacanthoides)、盐生假木贼群系(Form. Anabasis salsa)、沙生针茅群系(Form. Stipa glareosa)和白茎绢蒿群系(Form. Seriphidium terrae-albae)等9个主要群系, 并进一步划分出16个群丛。     

Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics

Next-generation sequencing (NGS) technologies have enabled the application of broad-scale sequencing in microbial biodiversity and metagenome studies. Biodiversity is usually targeted by classifying 16S ribosomal RNA genes, while metagenomic approaches target metabolic genes. However, both approaches remain isolated, as long as the taxonomic and functional information cannot be interrelated. Techniques like self-organizing maps (SOMs) have been applied to cluster metagenomes into taxon-specific bins in order to link biodiversity with functions, but have not been applied to broad-scale NGS-based metagenomics yet. Here, we provide a novel implementation, demonstrate its potential and practicability, and provide a web-based service for public usage. Evaluation with published data sets mimicking varyingly complex habitats resulted into classification specificities and sensitivities of close to 100% to above 90% from phylum to genus level for assemblies exceeding 8 kb for low and medium complexity data. When applied to five real-world metagenomes of medium complexity from direct pyrosequencing of marine subsurface waters, classifications of assemblies above 2.5 kb were in good agreement with fluorescence in situ hybridizations, indicating that biodiversity was mostly retained within the metagenomes, and confirming high classification specificities. This was validated by two protein-based classifications (PBCs) methods. SOMs were able to retrieve the relevant taxa down to the genus level, while surpassing PBCs in resolution. In order to make the approach accessible to a broad audience, we implemented a feature-rich web-based SOM application named TaxSOM, which is freely available at http://www.megx.net/toolbox/taxsom. TaxSOM can classify reads or assemblies exceeding 2.5 kb with high accuracy and thus assists in linking biodiversity and functions in metagenome studies, which is a precondition to study microbial ecology in a holistic fashion.     

山西庞泉沟国家自然保护区种子植物区系研究

庞泉沟自然保护区共有种子植物89科359属809种,其中裸子植物2科6属7种,被子植物87科353属802种。含5属以上的科15科,单、寡种属共330属,这些科、属在植物区系组成中具有重要作用。种子植物科、属、种的区系地理成分复杂多样。科的分布区类型中,世界分布型40科,北温带分布型24科,分别占总科数的44.94%和26.97%。属的分布区类型中,温带成分256属,占总属数的82.05%,反映出该区植物区系的温带性质。种的分布区类型中,中国特有分布种居首位,尤以华北成分占优势,其中华北落叶松、黄刺玫、油松等为该区植被建群种或优势种。科、属、种的特有现象不平衡,没有中国特有科,中国特有属、特有种分别为7属、325种。