森林害虫发生的空间模式研究进展

森林昆虫种群表现出多样的时空模式,空间同步性是其中最常见的。回顾了森林昆虫空间同步性的特点、形成机制及研究方法方面的进展。森林害虫发生的同步性是广泛存在的,但不同昆虫种类的同步性大小不同。空间同步性常随距离的增大而下降,还与时间尺度有关。Moran效应和扩散是解释空间同步性的两种主要机制,通常Moran效应的影响要比扩散大。从虫害发生数据的获取、同步性的度量及成因3个方面介绍了空间同步性的研究方法方面的进展。利用树轮生态学原理重建森林虫害发生历史的方法可在事后获取可靠的数据,很值得国内研究者借鉴和应用。在空间自相关度量上,空间统计学方法和地统计学方法都是非常有力的手段,但由于不能处理多时间点数据而限制了其在同步性研究中的应用。在同步性成因研究中,利用变异分解将基于距离的Moran特征向量图(dbMEM)作为空间变量研究害虫发生的驱动力是比较新颖的研究方法。     

长白山山杨种群的性比格局及其空间分布

以 5.2 hm²固定监测样地内山杨雌、雄植株的长期定位观测数据为基础,分析了长白山次生杨桦林内山杨(Populus davidiana)种群的性比格局、空间分布及其与环境因子间的关系。结果表明:山杨雌树和雄树的胸径大小差异不显著,始花大小相近,雄树的生长速率大于雌树,成熟个体的性比不显著偏离1 ∶ 1。山杨同性植株趋于相邻,雌雄植株的空间分离不显著;O-ring单变量点格局分析显示山杨在空间上呈异质性泊松分布。山杨同性别大树与小树在＜18m尺度上呈空间正相关;不同性别之间空间独立,说明性别之间的资源竞争不激烈。山杨雌树和雄树的空间分布与林分密度极显著正相关,林分密度对山杨雌雄植株空间分布变异的解释量较小,空间变量对雌树分布的解释量较大。     

金沙江干旱河谷植物群落的数量分类及其结构分异的环境解释

植物群落的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构建进程。本文基于金沙江流域干旱河谷116个样点562个样方的植物群落调查数据, 采用自适应仿射传播聚类的方法进行群落数量分类, 运用莫兰特征向量地图, 和方差分解的方法对影响群落结构的空间和环境因子进行分析。结果表明: (1)自适应仿射传播聚类将金沙江干旱河谷的植物群落分为30组, 可归为7个植被型, 23个群系, 以稀树草原(30.0%)、暖性落叶阔叶灌丛(55.7%)为最主要的植被类型。(2)年均温和干燥指数是限制金沙江干旱河谷植物群落分布的主要环境因子。稀树草原、肉质灌丛、常绿阔叶灌丛是典型的干热河谷植被类型; 暖性落叶阔叶灌丛、常绿硬叶林是干暖河谷植被的优势类型; 暖性针叶林、落叶阔叶林则主要在干温河谷环境占优势。(3)纯环境因子可以解释群落物种组成变化的5.5%, 纯空间因子可以解释的物种组成变化为22.5%, 有空间结构的环境因子部分为6.6%, 未解释的部分为65.4%。在诸多环境因子中, 年均温及干燥指数的不同显示了不同群落生境的重要差异, 并显著影响到群落的分布格局。大尺度的空间因子则主要通过地理隔离对群落结构的差异产生影响。     

Statistical methods for temporal and space–time analysis of community composition data

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran''s eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.     

Genetic structure of the critically endangered endemic Cochlearia polonica (Brassicaceae): efficiency of the last-chance transplantation

Cochlearia polonica , a narrow endemic of southern Poland, is one of the rarest and most endangered species of the European flora. All natural populations are extinct and the species has survived in only one transplanted population derived from 14 original individuals. Using AFLPs, the genetic variation and spatial structure of this population were analysed approximately 30 years after transplantation. The incidence of polymorphic AFLP bands (30.46%) is low compared with data from a natural population of another Cochlearia species, C. tatrae . Principal co-ordinates and spatial autocorrelation analyses demonstrated the presence of significant genetic structure. It is recommended that conservation efforts on C. polonica should preserve the complete population area, because local extinctions may lead to a loss of genetic information. The presence of genetic structure should also be taken into account during the sampling of material (plants or seeds) for ex situ conservation measures.  © 2007 The Linnean Society of London, Botanical Journal of the Linnean Society , 2007, 155 , 527–532.     

海南霸王岭陆均松空间分布格局及其与微生境异质性的关系

应用地统计学及限制性排序分析方法,对海南霸王岭陆均松(Dacrydium pectinatum)幼苗、幼树、成树及其微生境因子的空间分布特征进行分析。结果表明:1)陆均松不同生活史阶段空间分布格局存在明显差异,幼苗阶段表现为聚集性分布,具有显著的空间自相关结构,聚集强度随年龄增长呈逐渐减弱趋势;2)RDA分析表明,陆均松空间分布格局与微生境因子的关系在不同生活史阶段存在差异,生境过滤的影响会随着生活史阶段的不同而有所差别;3)微生境变量对陆均松幼苗、幼树、成树空间变异的总解释量分别为78.4%、41.2%、33.6%,微生境因素对陆均松分布的限制作用主要集中在幼苗阶段;4)在大尺度条件下,PCNM变量与微生境变量具有较大的共同解释量(22.2%),存在典型的\"诱导空间变差\"现象,微生境变量空间变化引起响应变量即陆均松幼苗、幼树、成树的分布产生类似的空间结构。研究结果证实了微生境因子与不同生活史阶段陆均松分布的关联性,为深入理解陆均松种群维持机制、空间分布格局、更新动态与生境的关系提供科学依据。     

Assessing temporal scales and patterns in time series: Comparing methods based on redundancy analysis

Time-series modelling techniques are powerful tools for studying temporal scaling structures and dynamics present in ecological and other complex systems and are gaining popularity for assessing resilience quantitatively. Among other methods, canonical ordinations based on redundancy analysis are increasingly used for determining temporal scaling patterns that are inherent in ecological data. However, modelling outcomes and thus inference about ecological dynamics and resilience may vary depending on the approaches used. In this study, we compare the statistical performance, logical consistency and information content of two approaches: (i) asymmetric eigenvector maps (AEM) that account for linear trends and (ii) symmetric distance-based Moran's eigenvector maps (MEM), which requires detrending of raw data to remove linear trends prior to analysis. Our comparison is done using long-term water quality data (25 years) from three Swedish lakes. This data set therefore provides the opportunity for assessing how the modelling approach used affects performance and inference in time series modelling. We found that AEM models had consistently more explanatory power than MEM, and in two out of three lakes AEM extracted one more temporal scale than MEM. The scale-specific patterns detected by AEM and MEM were uncorrelated. Also individual water quality variables explaining these patterns differed between methods, suggesting that inferences about systems dynamics are dependent on modelling approach. These findings suggest that AEM might be more suitable for assessing dynamics in time series analysis compared to MEM when temporal trends are relevant. The AEM approach is logically consistent with temporal autocorrelation where earlier conditions can influence later conditions but not vice versa. The symmetric MEM approach, which ignores the asymmetric nature of time, might be suitable for addressing specific questions about the importance of correlations in fluctuation patterns where there are no confounding elements of linear trends or a need to assess causality.