小兴安岭阔叶红松林木本植物种-面积关系

种-面积关系研究是了解植物群落结构的重要途径,是群落生态学的基本问题。不同的研究方法对种-面积关系影响很大。利用黑龙江省小兴安岭两个10.4 hm2样地和5个1.0 hm2样地的调查数据,采用移动窗口法确定各样地的最小取样面积,避免了巢式取样法及随机样方法的不足。并采用4种种-面积关系模型进行拟合,评价各关系模型的适合度。在此基础上,基于最小面积进行模拟随机取样,探讨取样大小对物种数估计精度的影响。研究结果表明:由于拟合曲线模型的适用性及曲线外推可靠性问题的存在,采用拟合曲线的方法所估计的最小面积与实际值偏差较大。实际调查得到的各样地最小面积40 m×40 m—45 m×45 m,说明小兴安岭地区阔叶红松林群落所需的最小面积基本一致,但各样地群落结构的差异却在对取样数量的要求上体现出来。其中丰林与大亮子河样地物种数分布相对均匀,所需最小样方数量较少;而方正与胜山样地物种数分布异质性较大,差异的机理还有待于进一步研究。     

城市植被调查的取样面积推算与遗漏植物曲线

种—面积关系常用于确定自然植被调查的取样面积, 但其在城市植被中的应用依然少见报道。基于重庆市3个行政区共54个样地, 采用巢式样方法和随机样方法同时调查样地所有植物, 揭示城市植物种—面积关系, 分析城市植物调查的取样面积推算方法, 并通过遗漏曲线揭示两种调查方法遗漏植物的规律。研究结果表明: 巢式样方法下, 种—面积曲线符合Logistic函数和Allometrica1函数的变化规律(R2>0.90), 相关公式可用于推算最小取样面积, 且取样精度越高则所需最小取样面积增加量越大。公园及居住区绿地, 调查到植物种数的比例从60%逐渐增加到90%时, 所需最小取样面积的平均值从17.7 m2逐渐增加到162.0 m2。在巢式样方法下, 取样面积从100 m2增加到625 m2, 公园和居住区绿地中遗漏的植物(未被调查到的植物), 种数比例分别从15.17%和13.98%降低至1.42%和0.42%。目前城市植物调查中常用的100 m2样方面积下, 公园和居住区中遗漏的物种中, 分别有78.1%和81.8%为频率3.7%的低频物种。公园和居住区绿地中, 遗漏植物的频率—种数关系均符合Hyperb1函数曲线(R2>0.95)。草本植物调查中常用的随机样方法(3个1 m × 1 m样方), 遗漏草本植物的种数平均为公园草本植物的41.44%、居住区草本植物的49.58%, 其中A级频率物种分别占公园及居住区的93.48%和93.22%。随机样方法下, 公园和居住区绿地遗漏草本植物的频率—种数关系符合Logistic函数曲线(R2>0.94)。研究结果和方法可为城市植物多样性调查取样方法的确定和评价提供一定的理论参考。     

具有左截断、右删失寿命数据类型的生命表编制方法

左截断数据是一类特殊的寿命数据类型,其定义为一些动物个体并非在初始时间（出生或孵化）而是在某个时间（年龄）延滞之后才进入调查取样范围而收集到的一类寿命数据。传统的乘积限估计法只能处理寿终数据和右删失数据,对左截断数据则无能为力。本文提出一种对乘积限估计法改进方法,此种方法能同时处理寿终数据、右删失数据和左截断数据,从而有效地利用了左截断数据所含有的物种存活信息。     

吉林蛟河阔叶红松林样地种-面积关系

种-面积关系是群落生态学的核心问题之一,是生物多样性尺度转换的重要依据。利用吉林蛟河阔叶红松林30 hm~2的样地数据,采用随机取样与巢式取样方法,分别在10、20、30 hm~2尺度上建立对数模型(Logarithmic function)、幂函数模型(Power function)和逻辑斯蒂模型(Logistic function)拟合局域种-面积关系,并利用赤池信息准则(AIC)进行拟合结果优度检验。结果表明,取样方法对种-面积关系的构建有显著影响,随机取样优于巢式取样。种-面积关系的构建与尺度(取样上限)密切相关:在小尺度上(10 hm~2),对数模型与逻辑斯蒂模型拟合效果优于幂函数模型;在中尺度和大尺度上(20、30 hm~2),相对于对数模型和幂函数模型,逻辑斯蒂模型能更好地拟合阔叶红松林的种-面积关系。据AIC值可知,随机取样下的逻辑斯蒂模型拟合效果最好,是拟合30 hm~2阔叶红松林样地种-面积关系的最适模型。因此研究时需要根据区域森林群落的实际情况选择种-面积模型。     

福建中亚热带常绿阔叶林(米槠林)最小面积的确定

通过“种-面积曲线”,“群落系数-面积曲线”以及“重要值-面积曲线”3种方法对福建中亚热带常绿阔叶林（米储林）的最小面积进行研究。结果表明,用3种方法确定的群落最小面积基本下相同。对所研究的植被类型。样地面积为400m^2时,可包括整个群落60%的物种数目;样地面积为1000m^2时,则可包括整个群落90%的物种数目,确定最小面积时应比计算出的理论面积稍大一些为宜,即样地面积大小为1200m^2。     

民族植物学定量研究中的取样方法

定量方法在民族植物学研究中越来越占有重要的地住,但民族植物学定量研究中有关取样方面还存在着一些不同的看法。笔者针对云南省金平苗族瑶族傣族自治县的拉祜族民间草医所使用的药用植物资源和草医对不同生境中药用植物的利用情况进行了取样,并以此对民族植物学中有关取样中存在的问题进行了讨论。结果表明不同生境中民族植物学的取样面积与植物生态学中的取样面积比较相近。所以在进行民族植物学野外取样时,司以针对具体的生境和植被特点来借用植物生态学中对该类生境或植被所采用的最小取样面积。     

植被科学与多元向量分析

提出植被分析的新数学方法－－多元向量分析法。植被群落是由多种植物组成的。植被状态可以用多维物种空间的点,或连接空间点和原点的多元向量来,向量同时具有量值和方向。向量的量值（长度）表示植被所含物质,能量,信息的总量,而方向表示这个总量在各物种间的分配。在射影空间里,同一射线上的点表示成分相同的植被（或代表相同植被的点组成射线）。用余弦表示的向量方向是植被数量化,进而施行植被成分分析的关键。向量分析既可以用来进行植被分类,也可以用于植被动态监测。     

武夷山山顶草甸植被取样技术探讨

本文主要探讨武夷山自然保护区山顶草甸植被调查样地的最小面积和样地的取样(分布)方式。样地最小面积为O.20平方米,更精确的为0.20-0.24平方米。代表性样地取样方式要注意避开人为因素影响。随机取样和规则(系统)取样各15个0.04平方米(共0.6平方米)样地得到的结果,与2-3个1平方米典型样地的调查结果相类似,并且测得较多的植物种类,避免了由于优势种不均匀分布而引起调查结果的夸大或缩小。规则取样简便,效果理想。     

岛屿栖息地鸟类群落的丰富度及其影响因子

1997年1月至1997年12月间,以杭州市的园林鸟类群落为研究对象,对岛屿栖息地鸟类群落的丰富度与面积,人为干扰,内部结构和周围景观结构等多种因素的关系进行了系统的分析和检验。在杭州市各园林中共观察到82种鸟类。园林单次调查的鸟类物种数（S）与园林全年总物种数（Sy)与园林面积（A）的最佳回归拟合方程分别为;S＝ 2.7432A^0.3846,Sy=10.6574A^0.3669。杭州市园林鸟类群落物种－面积关系的成因不支持平衡假说,随机取样假说,栖息地多样性假说和干扰假说,岛屿栖息地鸟类群落的丰富度是多因素综合作用的结果,包括取样面积效应（排除了取样面积效应之后,小园林具有更高的物种密度）,栖息地结构的多样性（其中树种多样性是最主要的影响因子）,干扰因素,物种因素和研究尺度等几个方面。     

取样尺度效应对滇西北地区种子植物物种多样性纬度分布格局的影响

滇西北地区是全球25个生物多样性保护热点地区之一, 是验证生物多样性理论的理想场所。为探索取样尺度效应对植物物种多样性纬度分布格局的影响, 我们探讨了不同取样尺度下滇西北地区种子植物物种多样性的纬度分布格局及其影响因子。我们利用野外考察数据和文献资料建立了群落尺度下的源数据集和区域尺度(县域尺度)下的源数据集, 共建立、收集了68个植物群落样方和26个县域的种子植物物种丰富度; 采用二元相关性和多元逐步回归分析植物物种多样性纬度分布格局与气候、地理因子间的关系。结果表明, 从南到北, 物种多样性在群落尺度下呈单调递减格局, 在区域尺度下反而呈线性递增趋势; 在群落尺度下受到热量因子的显著影响, 在区域尺度下主要受单位面积海拔高差的影响。这一结果在一定程度上表明了取样的尺度效应对物种多样性纬度分布格局的显著影响。了解滇西北地区植物多样性的热点区域, 应该基于不同取样尺度下的分析, 以消除或减少植物多样性保护的盲点。在今后的相关研究中, 应关注不同的取样尺度下多样性的纬度分布格局可能的表现形式及其内在机制, 这或许可以减少或消除相关研究中的争议或不一致。     

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species‐level Poisson processes and estimate patch‐level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early‐successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.     

样方大小对苔藓植物生态学指标的影响

为了分析样方大小对苔藓植物生态指标的影响,在环境相对一致的条件下,在各样点以巢式取样法调查苔藓植物盖度,取样的大小分别为20 cm× 20 cm,30 cm× 30 cm,40 cm×40 cm,50 cm× 50 cm,60 cm×60 cm.通过统计发现,随着取样面积的增加,目测法所获得的优势种、总的苔藓植物的盖度...     

Bias in vegetation databases? A comparison of stratified‐random and preferential sampling

Aim: Vegetation plots collected since the early 20th century and stored in large vegetation databases are an important source of ecological information. These databases are used for analyses of vegetation diversity and estimation of vegetation parameters, however such analyses can be biased due to preferential sampling of the original data. In contrast, modern vegetation survey increasingly uses stratified‐random instead of preferential sampling. To explore how these two sampling schemes affect vegetation analyses, we compare parameters of vegetation diversity based on preferentially sampled plots from a large vegetation database with those based on stratified‐random sampling. Location: Moravian Karst and Silesia, Czech Republic. Methods: We compared two parallel analyses of forest vegetation, one based on preferentially sampled plots taken from a national vegetation database and the other on plots sampled in the field according to a stratified‐random design. We repeated this comparison for two different regions in the Czech Republic. We focussed on vegetation properties commonly analysed using data from large vegetation databases, including alpha (within‐plot) diversity, cover and participation of different species groups, such as endangered and alien species within plots, total species richness of data sets, beta diversity and ordination patterns. Results: The preferentially sampled data sets obtained from the database contained more endangered species and had higher beta diversity, whereas estimates of alpha diversity and representation of alien species were not consistently different between preferentially and stratified‐randomly sampled data sets. In ordinations, plots from the preferential samples tended to be more common at margins of plot scatters. Conclusions: Vegetation data stored in large databases are influenced by researcher subjectivity in plot positioning, but we demonstrated that not all of their properties necessarily differ from data sets obtained by stratified‐random sampling. This indicates the value of vegetation databases for use in biodiversity studies; however, some analyses based on these databases are clearly biased and their results must be interpreted with caution.     

Effects of sampling protocol on the shapes of species richness curves

Aim Scheiner (Journal of Biogeography, 2009, 36 , 2005–2008) criticized several issues regarding the typology and analysis of species richness curves that were brought forward by Dengler (Journal of Biogeography, 2009, 36 , 728–744). In order to test these two sets of views in greater detail, we used a simulation model of ecological communities to demonstrate the effects of different sampling schemes on the shapes of species richness curves and their extrapolation capability. Methods We simulated five random communities with 100 species on a 64 × 64 grid using random fields. Then we sampled species–area relationships (SARs, contiguous plots) as well as species–sampling relationships (SSRs, non‐contiguous plots) from these communities, both for the full extent and the central quarter of the grid. Finally, we fitted different functions (power, quadratic power, logarithmic, Michaelis–Menten, Lomolino) to the obtained data and assessed their goodness‐of‐fit (Akaike weights) and their extrapolation capability (deviation of the predicted value from the true value). Results We found that power functions gave the best fit for SARs, while for SSRs saturation functions performed better. Curves constructed from data of 32² grid cells gave reasonable extrapolations for 64² grid cells for SARs, irrespective of whether samples were gathered from the full extent or the centre only. By contrast, SSRs worked well for extrapolation only in the latter case. Main conclusions SARs and SSRs have fundamentally different curve shapes. Both sampling strategies can be used for extrapolation of species richness to a target area, but only SARs allow for extrapolation to a larger area than that sampled. These results confirm a fundamental difference between SARs and area‐based SSRs and thus support their typological differentiation.     

Randomvs non-random sampling: Effects on patterns of species abundance, species richness and vegetation-environment relationships

From a strictly statistical perspective, most of the commonly used statistical tests cannot be performed on vegetation data obtained using a non-random sampling design. Despite this, non-randomly sampled plots such as phytosociological relevés still make sense: because they may focus on objectives not appropriately addressed by random sampling, such as the study of rare plant communities or species; and because random sampling is often more time-demanding and expensive. Considering the huge body of phytosociological data available, an interesting question arises: if we compare randomly and non-randomly sampled data sets, to what extent do the results of our analyses differ with respect to various species and vegetation parameters? We present an attempt to tackle this question by comparing two data sets collected in a 25 km² area close to the city of Bremen, northwestern Germany: the first data set consisted of 30 subjectively (non-randomly) placed, homogeneous plots across different plant communities, each of which was laid out in a nested design including 9 sizes from 0.5 m² to 1,000 m². The second data set consisted of 30 (again nested) plots randomly selected and located with a GPS device; plots were rejected only if they for some reason were inaccessible. The data collection was the same for both data sets: presence-absence of all vascular plants was recorded for the different plot sizes, and soil samples were collected for the determination of the values of some important environmental variables. For the comparison of the two data sets, we used either the complete data sets or sub-sets of those plots located in woodlands. The main results included the following: (1) Species abundance patterns: Random sampling resulted in a larger number of common and a smaller number of rare species than non-random sampling. (2) Species richness at different spatial scales: For the small plot sizes, the number of species in the non-randomly placed plots was higher than in the randomly placed plots, while the differences were less pronounced at larger spatial scales. As a consequence, also the parameters of species-area curves differed between the data sets, especially in the sub-set including woodland plots. (3) Vegetation differentiation: In random sampling, there was considerable redundancy, i.e., there were several plots with high floristic similarity. (4) Vegetation-environment relationships: The ordination scores of the non-randomly placed plots showed a larger number of significant correlations to soil parameters than the scores of randomly placed plots. The results suggest that conclusions drawn from the analysis of non-randomly placed plots such as phytosociological relevés may be biased, especially regarding estimates of species abundance and species richness patterns.     

The relationship between psyllid leaf galls and redbay (Persea borbonia) fitness traits in sun and shade

Spatial autocorrelation in vegetation has been discussed extensively, but little is yet known about how standard plant sampling methods perform when confronted with varying levels of patchiness. Simulated species maps with a range of total abundance and spatial autocorrelation (patchiness) were sampled using four methods: strip transect, randomly located quadrats, the non-nested multiscale modified Whittaker plot and the nested multiscale North Carolina Vegetation Survey (NCVS) plot. Cover and frequency estimates varied widely within and between methods, especially in the presence of high patchiness and for species with moderate abundances. Transect sampling showed the highest variability, returning estimates of 19–94% cover for a species with an actual cover of 50%. Transect and random methods were likely to miss rare species entirely unless large numbers of quadrats were sampled. NCVS plots produced the most accurate cover estimates because they sampled the largest area. Total species richness calculated using semilog species-area curves was overestimated by transect and random sampling. Both multiscale methods, the modified Whittaker and the NCVS plots, overestimated species richness when patchiness was low, and underestimated it when patchiness was high. There was no clear distinction between the nested NCVS or the non-nested modified Whittaker plot for any of the measures assessed. For all sampling methods, cover and especially frequency estimates were highly variable, and depended on both the level of autocorrelation and the sampling method used. The spatial structure of the vegetation must be considered when choosing field sampling protocols or comparing results between studies that used different methods.     

Random placement models predict species–area relationships in duck communities despite species aggregation

Species–area relationships are the product of many ecological processes and their interactions. Explanations for the species–area relationship (SAR) have focused on separating putative niche‐based mechanisms that correlate with area from sampling effects caused by patches with more individuals containing more species than patches with fewer individuals. We tested the hypothesis that SARs in breeding waterfowl communities are caused by sampling effects (i.e. random placement from the regional species pool). First, we described observed SARs and patterns of species associations for fourteen species of ducks on ponds in prairie Canada. Second, we used null models, which randomly allocated ducks to ponds, to test if observed SARs and patterns of species associations differed from those expected by chance. Consistent with the sampling effects hypothesis, observed SARs were accurately predicted by null models in three different years and for diving and dabbling duck guilds. This is the first demonstration that null models can predict SARs in waterbirds or any other aquatic organisms. Observed patterns of species association, however, were not well predicted by null models as in all years there was less observed segregation among species (i.e. more aggregation) than under the random expectation, suggesting that intraspecific competition could play a role in structuring duck communities. Taken together, our results indicate that when emergent properties of ecological communities such as the SAR appear to be caused by random processes, analyses of species associations can be critical in revealing the importance of niche‐based processes (e.g. competition) in structuring ecological communities.     

Shape matters in sampling plant diversity: Evidence from the field

The identification of shape and size of sampling units that maximises the number of plant species recorded in multiscale sampling designs has major implications in conservation planning and monitoring actions. In this paper we tested the effect of three sampling shapes (rectangles, squared, and randomly shaped sampling units) on the number of recorded species. We used a large dataset derived from the network of protected areas in the Siena Province, Italy. This dataset is composed of plant species occurrence data recorded from 604 plots (10 m × 10 m), each divided in a grid of 16 contiguous subplot units (2.5 m × 2.5 m). Moreover, we evaluated the effect of plot orientation along the main environmental gradient, to examine how the selection of plot orientation (when elongated plots are used) influences the number of species collected. In total, 1041 plant species were recorded from the study plots. A significantly higher species richness was recorded by the random arrangement of 4 subplots within each plot in comparison to the ‘rectangle’ and ‘square’ shapes. Although the rectangular shape captured a significant larger number of species than squared ones, plot orientation along the main environmental gradient did not show a systematic effect on the number of recorded species. We concluded that the choice of whether or not using elongated (rectangular) versus squared plots should dependent upon the objectives of the specific survey with squared plots being more suitable for assessing species composition of more homogeneous vegetation units and rectangular plots being more suited for recording more species in the pooled sample of a large area.     

Influence of sampling strategy on the relationships between fauna and vegetation structure,plant lifeform and floristics

The effect of sampling strategy on animal-habitat relationships was evaluated with data collected from a 50 ha area containing a sequence of tropical vegetation types. Sampling sites were located randomly within defined habitat types (i.e. stratified random sampling) and systematically irrespective of habitat type. At each site the fauna, comprising birds (63 species), reptiles (15 species), amphibia (13 species) and grasshoppers (32 species) were sampled for the abundance of species. Simultaneously, vegetation and related data, comprising vertical structure (52 attributes), ground surface condition (18 attributes), plant lifeform (18 attributes) and the abundance of plant species (200) were recorded. Random and systematic data matrices, comprising sites defined by animal or vegetation attributes, were reduced dimensionally by correspondence analysis. Animal first dimension vectors were then regressed on the first dimension vectors of vegetation structure, lifeform and floristics, respectively. With stratified random sampling, vegetation structure (comprising vertical and ground attributes) and lifeform explained most of the variation in the fauna; floristics were not a significant factor. On the other hand with the systematic data, fioristics explained almost all of the variation in animal abundance and distribution. By removing the ecotonal sites from the systematic data set and recalculating vectors, the animal—vegetation relationships became similar to those generated from the stratified random sampling data. Clearly, the sampling strategy employed in a faunal survey has a major influence on the inventory of species, and on the relative importance of vegetation structure, lifeform and floristics in explaining animal distribution. The presence of ecotones in the systematic data set was highlighted as the key to the difference between the sampling strategies.