栽培药材川续断地上部分体积估算的最优样方选择

以贵州省黔南州龙里县内人工栽培的川续断(Dipsacus asperoides)为研究对象, 就如何选择最优样方的面积和数目来估算川续断地上部分体积进行了研究。首先运用球缺模型计算栽培川续断的地上体积, 然后利用基于套状样方的样带调查法研究估算川续断体积时的最优样方面积, 最后利用方差法计算最优采样数目。结果表明: (1)在只考虑相对平均值和相对消耗时, 25 m × 25 m是最优样方面积; 在此基础上进一步考虑到样方的边界效应和单位面积地上体积相对平均值的变化, 得出25 m × 50 m是最优样方面积。(2)如果预计置信度1-α是0.9, 绝对误差限度d是0.12, 总体方差S²按照常规取0.25, 则25 m × 50 m对应的最佳样方数目是25。(3)该研究实际采集了25个25 m × 50 m的样方, 计算后得到整个栽培园地(面积为72696.24 m ²)川续断的总体积90%的近似置信区间为[1909.798 m³, 2214.762 m³]。     

An evaluation of the state of spatial point pattern analysis in ecology

Over the last two decades spatial point pattern analysis (SPPA) has become increasingly popular in ecological research. To direct future work in this area we review studies using SPPA techniques in ecology and related disciplines. We first summarize the key elements of SPPA in ecology (i.e. data types, summary statistics and their estimation, null models, comparison of data and models, and consideration of heterogeneity); second, we review how ecologists have used these key elements; and finally, we identify practical difficulties that are still commonly encountered and point to new methods that allow current key questions in ecology to be effectively addressed. Our review of 308 articles published over the period 1992–2012 reveals that a standard canon of SPPA techniques in ecology has been largely identified and that most of the earlier technical issues that occupied ecologists, such as edge correction, have been solved. However, the majority of studies underused the methodological potential offered by modern SPPA. More advanced techniques of SPPA offer the potential to address a variety of highly relevant ecological questions. For example, inhomogeneous summary statistics can quantify the impact of heterogeneous environments, mark correlation functions can include trait and phylogenetic information in the analysis of multivariate spatial patterns, and more refined point process models can be used to realistically characterize the structure of a wide range of patterns. Additionally, recent advances in fitting spatially‐explicit simulation models of community dynamics to point pattern summary statistics hold the promise for solving the longstanding problem of linking pattern to process. All these newer developments allow ecologists to keep up with the increasing availability of spatial data sets provided by newer technologies, which allow point patterns and environmental variables to be mapped over large spatial extents at increasingly higher image resolutions.     

Interpretation of scale in paired quadrat variance methods

Question: Previous interpretations of the variance plot of paired quadrat variance method (PQV) have been incomplete. The objective of this study was to clarify the interpretation of PQV, and to shed additional light on how different quadrat variance methods can be used, in concert, to measure scale in transect data. Methods: We used artificial and real data to examine how the PQVmethod elucidates spatial pattern. Two‐term local quadrat variance (TTLQV) and new local variance (NLV) methods, together with their three‐term counterparts, were also applied to the same data sets, and the results from all methods were compared. Results: When the mean gap size equalled the mean patch size along a transect, the first peak of the variance of PQV, NLV and TTLQV corresponded with the gap size (or patch size). However, if the mean gap size and patch size were unequal, the variance plot of PQV displayed a flat‐topped plateau, in which the first inflection represented the mean size of the smaller phase and the second inflection represented the mean size of the larger phase; TTLQV showed a clear peak and NLV displayed a distinct first peak while the second inflection was dampened. The results also indicated than the three‐term versions of quadrat variance methods did not consistently outperform their two‐term counterparts, and often confused the interpretation of scale. Conclusions: The quadrat variance methods associated with the patch‐gap measurements were able to efficiently detect not only the size of patches, but also the size of gaps.     

Useful surrogates of soil texture for plant ecologists from airborne gamma‐ray detection

Plant ecologists require spatial information on functional soil properties but are often faced with soil classifications that are not directly interpretable or useful for statistical models. Sand and clay content are important soil properties because they indicate soil water‐holding capacity and nutrient content, yet these data are not available for much of the landscape. Remotely sensed soil radiometric data offer promise for developing statistical models of functional soil properties applicable over large areas. Here, we build models linking radiometric data for an area of 40,000 km² with soil physicochemical data collected over a period of 30 years and demonstrate a strong relationship between gamma radiometric potassium (⁴⁰K), thorium (²³²Th), and soil sand and clay content. Our models showed predictive performance of 43% with internal cross‐validation (to held‐out data) and ~30% for external validation to an independent test dataset. This work contributes to broader availability and uptake of remote sensing products for explaining patterns in plant distribution and performance across landscapes.     

Variance and spatial scales in a tropical rain forest: changing the size of sampling units

The size of a sampling unit has a critical effect on our perception of ecological phenomena; it influences the variance and correlation structure estimates of the data. Classical statistical theory works well to predict the changes in variance when there is no autocorrelation structure, but it is not applicable when the data are spatially autocorrelated. Geostatistical theory, on the other hand, uses analytical relationships to predict the variance and autocorrelation structure that would be observed if a survey was conducted using sampling units of a different size. To test the geostatistical predictions, we used information about individual tree locations in the tropical rain forest of the Pasoh Reserve, Malaysia. This allowed us to simulate and compare various sampling designs. The original data were reorganised into three artificial data sets, computing tree densities (number of trees per square meter in each quadrat) corresponding to three quadrat sizes (5×5, 10×10 and 20×20 m⁽²⁾). Based upon the 5×5 m⁽²⁾ data set, the spatial structure was modelled using a random component (nugget effect) plus an exponential model for the spatially structured component. Using the within-quadrat variances inferred from the variogram model, the change of support relationships predicted the spatial autocorrelation structure and new variances corresponding to 10×10 m⁽²⁾ and 20×20 m⁽²⁾ quadrats. The theoretical and empirical results agreed closely, while the classical approach would have largely underestimated the variance. As quadrat size increases, the range of the autocorrelation model increases, while the variance and proportion of noise in the data decrease. Large quadrats filter out the spatial variation occurring at scales smaller than the size of their sampling units, thus increasing the proportion of spatially structured component with range larger than the size of the sampling units.     

Measures of spatial heterogeneity for species occurrence or disease incidence with finite-counts

We propose two types of indices with finite-count correction to measure the spatial heterogeneity of binary characteristics of organisms, such as occurrence or non-occurrence of organisms and infected or non-infected plants. We consider the following two examples: plant occurrence in a grassland community, and yellow dwarf disease infection in a rice field. For the grassland community, N quadrats comprising n cells of equal area, were set at random sites in a grassland, and the occurrence of a given species A in each of n cells was recorded. For disease infection, N quadrats, each consisting of n rice plants, were set at random sites in a paddy field, and the number of plants infected with yellow dwarf virus in each quadrat were counted. In these examples, since the number of cells in a quadrat is finite, neither occurrence nor incidence increase infinitely, unlike the number of aphids on a maize leaf. The first category of index belongs to the mean : variance ratio type. The estimated index value for occurrence (or incidence) is the same as that for non-occurrence (or non-incidence). The second category belongs to the k-type of a negative binomial distribution. If some random plants die or recover from the disease then the expected value of the second type of index does not change. For n, the current indices approach the mean : variance ratio and the inverse of k in a negative binomial distribution, respectively. This indicates that these indices are suitable for binary data sets.     

Bird and mammal species composition in distinct geographic regions and their relationships with environmental factors across multiple spatial scales

Global patters of species distributions and their underlying mechanisms are a major question in ecology, and the need for multi‐scale analyses has been recognized. Previous studies recognized climate, topography, habitat heterogeneity and disturbance as important variables affecting such patterns. Here we report on analyses of species composition – environment relationships among different taxonomic groups in two continents, and the components of such relationships, in the contiguous USA and Australia. We used partial Canonical Correspondence Analysis of occurrence records of mammals and breeding birds from the Global Biodiversity Information Facility, to quantify relationships between species composition and environmental variables in remote geographic regions at multiple spatial scales, with extents ranging from 10⁵ to 10⁷ km² and sampling grids from 10 to 10,000 km². We evaluated the concept that two elements contribute to the impact of environmental variables on composition: the strength of species' affinity to an environmental variable, and the amount of variance in the variable. To disentangle these two elements, we analyzed correlations between resulting trends and the amount of variance contained in different environmental variables to isolate the mechanisms behind the observed relationships. We found that climate and land use‐land cover are responsible for most explained variance in species composition, regardless of scale, taxonomic group and geographic region. However, the amount of variance in species composition attributed to land use / land cover (LULC) was closely related to the amount of intrinsic variability in LULC in the USA, but not in Australia, while the effect of climate on species composition was negatively correlated to the variability found in the climatic variables. The low variance in climate, compared to LULC, suggests that species in both taxonomic groups have strong affinity to climate, thus it has a strong effect on species distribution and community composition, while the opposite is true for LULC.     

Spatial ecology of jaguars,pumas, and ocelots: a review of the state of knowledge

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The consequences of spatial structure for the design and analysis of ecological field surveys

In ecological field surveys, observations are gathered at different spatial locations. The purpose may be to relate biological response variables (e.g., species abundances) to explanatory environmental variables (e.g., soil characteristics). In the absence of prior knowledge, ecologists have been taught to rely on systematic or random sampling designs. If there is prior knowledge about the spatial patterning of the explanatory variables, obtained from either previous surveys or a pilot study, can we use this information to optimize the sampling design in order to maximize our ability to detect the relationships between the response and explanatory variables?
The specific questions addressed in this paper are: a) What is the effect (type I error) of spatial autocorrelation on the statistical tests commonly used by ecologists to analyse field survey data? b) Can we eliminate, or at least minimize, the effect of spatial autocorrelation by the design of the survey? Are there designs that provide greater power for surveys, at least under certain circumstances? c) Can we eliminate or control for the effect of spatial autocorrelation during the analysis? To answer the last question, we compared regular regression analysis to a modified t‐test developed by Dutilleul for correlation coefficients in the presence of spatial autocorrelation.
Replicated surfaces (typically, 1000 of them) were simulated using different spatial parameters, and these surfaces were subjected to different sampling designs and methods of statistical analysis. The simulated surfaces may represent, for example, vegetation response to underlying environmental variation. This allowed us 1) to measure the frequency of type I error (the failure to reject the null hypothesis when in fact there is no effect of the environment on the response variable) and 2) to estimate the power of the different combinations of sampling designs and methods of statistical analysis (power is measured by the rate of rejection of the null hypothesis when an effect of the environment on the response variable has been created).
Our results indicate that: 1) Spatial autocorrelation in both the response and environmental variables affects the classical tests of significance of correlation or regression coefficients. Spatial autocorrelation in only one of the two variables does not affect the test of significance. 2) A broad‐scale spatial structure present in data has the same effect on the tests as spatial autocorrelation. When such a structure is present in one of the variables and autocorrelation is found in the other, or in both, the tests of significance have inflated rates of type I error. 3) Dutilleul's modified t‐test for the correlation coefficient, corrected for spatial autocorrelation, effectively corrects for spatial autocorrelation in the data. It also effectively corrects for the presence of deterministic structures, with or without spatial autocorrelation.
The presence of a broad‐scale deterministic structure may, in some cases, reduce the power of the modified t‐test.     

海南尖峰岭不同热带雨林类型与物种多样性变化关联的环境因子

环境因子是影响物种分布并导致物种多样性形成的重要因素,采伐后恢复的热带森林次生林和原始林的环境因子是否一致是一个很重要的问题.对于该问题的回答对长期监测热带森林次生林的变化具有重要意义.该文基于在海南尖峰岭地区设置的164个625 m2植被公里网格样地数据,记录了每个样地的采伐历史并测定了其他的17个环境变量指标,分析了17个环境因子之间的相关关系;将164个样地划分成3种不同采伐历史的森林,通过典范对应分析(CCA)探讨3种森林类型中影响物种分布的环境因子组成;比较两种多元回归模型的优劣,来揭示3种森林类型中影响物种丰富度形成的环境因子组成的差异.结果表明:驱动海南尖峰岭地区物种分布并导致物种多样性差异的环境因子在森林采伐前后并不是一成不变的,而是与森林采伐历史有关联的.除了人为森林采伐干扰外,海拔梯度是形成海南尖峰岭热带天然林物种多样性的最重要因素.CCA分析显示:原始林中,物种分布与海拔、土壤交换性钙和交换性镁含量3个环境因子有较密切的关系,也与4个土壤物理性质环境因子(土壤密度、土壤最大持水能力、毛细管持水量和毛管孔隙度)关系密切;森林采伐后的恢复森林中,土壤全磷和速效磷含量对物种分布的影响增强,但皆伐后土壤交换性钙和交换性镁含量对物种分布的影响减弱.多元回归分析显示:原始林的物种丰富度与海拔和土壤交换性钙含量显著相关,径级择伐后恢复热带天然林的物种丰富度和海拔、土壤全磷含量和速效钾含量显著相关,皆伐后恢复热带天然林的物种丰富度仅和海拔显著相关.研究结果还显示,如果数据中存在空间自相关,建立多元回归模型时应该考虑数据中的空间自相关属性,虽然它并不总是存在的.     

地质统计学在昆虫种群空间结构研究中的应用概述

地质统计学是用来人间相关变量结构的统计学方法,地质统计学中的变差函数和变差图,可分析空间变量在不同方向或不同环境条件下的空间结构。     

芦芽山亚高山草甸优势种群和群落的二维格局分析

种群和群落的二维空间格局研究能够更好地揭示群落的空间和结构特征,但在分析方法上有较大的困难。用垂直相交的两条样带在两个方向上同时取样的二维取样法,获得数据,用一维格局分析方法分别分析,可以得到各个种不同格局规模斑块的长、宽及面积,实现二维格局研究。用DCA排序和格局分析方法相结合,可以完成群落的二维格局分析。在山西芦芽山亚高山草甸应用的结果表明这样的垂直样带二维取样及分析方法较好地反映了种群和群落的空间特性,是非常有效的,并且该方法简单易做,具有较大的可操作性。所研究的草甸主要优势种格局斑块的形状比较规则,面积也较大。次要种斑块多为不规则形,面积也较小。群落格局与主要优势种的格局关系密切。     

Defining ecology: Ecological theories,mathematical models,and applied biology in the 1960s and 1970s

Ever since the early decades of this century, there have emerged a number of competing schools of ecology that have attempted to weave the concepts underlying natural resource management and natural-historical traditions into a formal theoretical framework. It was widely believed that the discovery of the fundamental mechanisms underlying ecological phenomena would allow ecologists to articulate mathematically rigorous statements whose validity was not predicated on contingent factors. The formulation of such statements would elevate ecology to the standing of a rigorous scientific discipline on a par with physics. However, there was no agreement as to the fundamental units of ecology. Systems ecologists sought to identify the fundamental organization that tied the physical and biological components of ecosystems into an irreducible unit: the ecosystem was their fundamental unit. Population ecologists sought, instead, to identify the biological mechanisms regulating the abundance and distribution of plant and animal species: to these ecologists, the individual organism was the fundamental unit of ecology, and the physical environment was nothing more than a stage upon which the play of individuals in perennial competition took place. As Joel Hagen has pointed out, the two schools were thus dividied by fundamentally different and irreconcilable assumptions about the nature of ecosystems.Notwithstanding these divisive efforts to elevate the image of ecology, the discipline remained in the shadows of American academia until the mid-1960s, when systems ecologists succeeded in projecting ecology onto the national scene. They did so by seeking closer involvement with practical problems: they argued before Congress that their approach to the theoretical problems of ecology was uniquely suited to the solution of the impending environmental crisis. With the establishment of the International Biological Program, they succeeded in attracting unprecedented levels of funding for systems ecology research. Theoretical population ecologists, on the other hand, found themselves consigned to the outer regions of this new institutional landscape. The systems ecologists' successful capture of the limelight and the purse brought the divisions between them and population ecologists into sharper relief — hence the hardening of the division of ecology observed by Hagen.⁴⁵     

Spatial Ecology of a Canada Lynx Population in Northern Maine

Abstract Canada lynx (Lynx canadensis) were listed as a federally threatened species in 14 states at the southern extent of their geographic range in March 2000, with Maine being the only state in the northeastern United States known to support a resident population. Relatively little information is known about the ecology of lynx living at the southern edge of their range, including range requirements, movements, and spatial organization. Basic knowledge of lynx ecology is needed for federal recovery planning efforts. Between 1999 and 2004, we trapped and radiocollared 43 lynx (21 M, 22 F) in northern Maine in an intensively managed and predominantly early successional forested landscape. We estimated diurnal annual and seasonal home-range size for male and female lynx using the 85% fixed-kernel home-range estimator. Annual home ranges of adult male lynx (x̄ = 53.6 km²) were more than twice the size of adult female home ranges (x̄ = 25.7 km²). Home ranges of adult females during snow periods (x̄ = 38.3 km²) were nearly 3 times larger than their snow-free-period ranges (x̄ = 14.3 km²), whereas, snow-free ranges of adult males (x̄ = 58.8 km²) were slightly larger than their snow-period ranges (x̄ = 45.2 km²). We observed a limited amount of home-range overlap among lynx of the same sex (F: x̄ = 17.2%; M: x̄ = 11.8%). Lynx of opposite sex showed more extensive overlap (x̄ = 24.3%). Most home-range shifts of resident lynx were typically not extensive. Based on territory mapping, we estimated a minimum lynx density of 9.2–13.0 lynx/100 km². We observed lynx spatial ecology and densities that were more similar to northern lynx populations when hares were abundant than to other southern lynx populations, suggesting that region-specific studies under varying habitat conditions and hare densities are needed to ensure realistic recovery goals and effective management of lynx at the southern extent of their range.     

Geostatistical approaches and optimal additional sampling schemes for spatial patterns and future sampling of bird diversity

Aim To evaluate geostatistical approaches, namely kriging, co‐kriging and geostatistical simulation, and to develop an optimal sampling design for mapping the spatial patterns of bird diversity, estimating their spatial autocorrelations and selecting additional samples of bird diversity in a 2450 km² basin. Location Taiwan. Methods Kriging, co‐kriging and simulated annealing are applied to estimate and simulate the spatial patterns of bird diversity. In addition, kriging and co‐kriging with a genetic algorithm are used to optimally select further samples to improve the kriging and co‐kriging estimations. The association between bird diversity and elevation, and bird diversity and land cover, is analysed with estimated and simulated maps. Results The Simpson index correlates spatially with the normalized difference vegetation index (NDVI) within the micro‐scale and the macro‐scale in the study basin, but the Shannon diversity index only correlates spatially with NDVI within the micro‐scale. Co‐kriging and simulated annealing simulation accurately simulate the statistical and spatial patterns of bird diversity. The mean estimated diversity and the simulated diversity increase with elevation and decrease with increasing urbanization. The proposed optimal sampling approach selects 43 additional sampling sites with a high spatial estimation variance in bird diversity. Main conclusions Small‐scale variations dominate the total spatial variation of the observed diversity due to a lack of spatial information and insufficient sampling. However, simulations of bird diversity consistently capture the sampling statistics and spatial patterns of the observed bird diversity. The data thus accumulated can be used to understand the spatial patterns of bird diversity associated with different types of land cover and elevation, and to optimize sample selection. Co‐kriging combined with a genetic algorithm yields additional optimal sampling sites, which can be used to augment existing sampling points in future studies of bird diversity.     

Preliminary bonobo and chimpanzee nesting by habitat type in the northern Lac Tumba Landscape,Democratic Republic of Congo

Knowing how habitat determines the distribution of great apes is essential for understanding their ecology and conservation requirements. Habitats in the northern Lac Tumba Landscape where this study was conducted are mostly swamp and flooded forests, which types have been overlooked in many great ape surveys. This study describes and discusses patterns of bonobo and chimpanzee nesting sites across these habitat types in the general scope of habitat use by great apes. Considerable efforts were deployed to survey forests of the Ngiri Triangle (186 km), Bomongo‐Lubengo (126 km) and Bolombo‐Losombo (112 km). Great ape nesting site encounter rates (r) were calculated for Bonobos (r = 0.21 nesting sites km^?1; Bolombo‐Losombo), chimpanzees (r = 0.11 nesting sites km^?1; Ngiri Triangle) and (r = 0.02 nesting sites km^?1; Bomongo‐Lubengo). Swamps and flooded forests dominated the three zones. Nesting sites were at the highest encounter rates in flooded forests; both great ape species were significantly associated with swampy and flood forests. Human signs did not influence the occurrence of nesting sites in these forests. These results confirm findings from other sites where great apes were observed using swamps; they suggest that future surveys include these types of habitat to avoid under‐estimating population sizes.     

吉林蛟河42 hm2针阔混交林样地植物种-面积关系

 种-面积关系是生态学中的基本问题, 其构建方式对种-面积关系的影响以及最优种-面积模型的选择仍然存在争议。该文利用吉林蛟河42 hm²针阔混交林样地数据, 分别采用巢式样方法和随机样方法建立对数模型、幂函数模型和逻辑斯蒂克模型, 并通过赤池信息量准则(AIC)检验种-面积模型优度。结果表明, 种-面积关系受到取样方法的影响, 随机样方法的拟合效果优于巢式样方法。采用随机样方法构建的幂指数模型(AIC = 89.11)和逻辑斯蒂克模型(AIC = 71.21)优于对数模型(AIC = 113.81)。根据AIC值可知, 随机样方法构建的逻辑斯蒂克模型是拟合42 hm²针阔混交林样地种-面积关系的最优模型。该研究表明: 在分析种-面积关系时不仅应考虑尺度效应, 还需注意生境变化及群落演替的影响。     

An evaluation of semi‐automated methods for collecting ecosystem‐level data in temperate marine systems

Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high‐resolution imaging and associated machine‐learning image‐scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free‐to‐use machine‐learning software to semi‐automatically generate dense and widespread abundance records of a habitat‐forming algae over ~5,000 m² of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver‐based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10–20 transects (50 × 1 m) were required to obtain reliable results. This represents 2–20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine‐resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi‐automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.     

Branching-diffusion model for the formation of distributional patterns in populations

A continuous time branching-diffusion process model is presented to describe the development of spatial distributional patterns of a biological population. In the model each unit moves independently following diffusion processes on a plane, and multiplies or goes extinct at random times. Standard methods for measuring the degree of aggregation used in field ecology are applied to this model population. Kuno's C _Aindex using quadrat sampling is calculated, and the dependence of the index on time, quadrat size, initial density, and diffusion and branching rules, is discussed. Pielou's index based on distance measurement is evaluated by the solution of a nonlinear partial differential equation. Both methods show that continuous-time branching-diffusion processes produce a contagious spatial pattern; as in a discrete-time model studied by Iwasa and Teramoto (1977).