生态学中的尺度问题：内涵与分析方法

尺度问题已成为现代生态学的核心问题之一.尺度问题主要涉及3个方面：尺度概念、尺度分析和尺度推绎.主要评述前两个方面.生态学尺度有三重概念：维数、种类和组分,其中每重概念又包含了多个定义,有必要进行澄清、分类和统一.尺度分析涉及尺度效应分析和多尺度空间格局分析.格局、过程及它们之间的关系,以及某些景观特性均表现出尺度效应,因此多尺度研究非常必要和重要.多尺度空间格局分析（尤其是特征尺度的识别）是进行尺度效应分析和跨尺度推绎的基础.多尺度分析需要特定的方法,景观指数法是最常用和最简单的方法,但也常产生误导;空间统计学方法（如半方差分析法、尺度方差分析法、空隙度指数法和小波分析法等）和分维分析法在最近十几年发展起来,并逐渐应用于生态学,在尺度分析上具有很大的应用潜力.各种方法在尺度分析上各有优势和不足,有必要同时使用两种或两种以上方法进行比较和评估.总之,有关尺度分析的研究需要进一步加强,从而为下一步的尺度推绎提供可靠的依据.     

Vegetation controls vary across space and spatial scale in a historic grassland‐forest biome boundary

Ecological boundaries are critical landscape regions of transition between adjacent ecological systems. While environmental controls of boundaries may operate in a scale‐dependent manner, multiple‐scale comparisons of vegetation–environment relationships have been characterized for few boundary systems. We used approximately 250 000 point records on the occurrence of woody versus grassland vegetation in conjunction with climatic, topographical, and soils data to evaluate scale effects and spatial heterogeneity in a 650‐km section of the historic prairie–forest biome boundary of Minnesota, USA. We chose this as a model system because of the availability of historical vegetation data, a considerable spatial extent, a sharp ecological transition, and the ability to avoid confounding from more recent anthropogenic land use change. We developed modeling techniques using hierarchical variance partitioning in a spatially‐structured format that allowed us to simultaneously evaluate vegetation–environment relationships across two‐dimensional space (i.e. the prairie‐forest boundary) and across spatial scales (i.e. varying extents). Soils variables displayed the least spatial autocorrelation at shortest lag distances and tended to be the least important predictors of woody vegetation at all spatial extents. Topographical variables displayed greater spatial heterogeneity in regions dominated by forest compared with prairie and were more important at fine‐intermediate spatial scales, highlighting their likely control on fire regimes. An integrated climatic variable (precipitation minus potential evapotranspiration) displayed a trend of increasing spatial variance across the study region and was unambiguously the strongest biome boundary control, although its joint influence with fire was difficult to characterize. Spatially heterogeneous vegetation–environment relationships were observed at all scales, especially at finer scales. Our results suggest that the importance of environmental controls changes smoothly rather than discretely across scales and demonstrate the need to account for spatial non‐stationarity and scale to predict and understand vegetation distribution across ecological boundaries.     

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.     

Implications of scale for patterns and processes in stream ecology

Abstract Although the scale-dependence of ecological patterns and processes is recognized by freshwater ecologists, current knowledge of scale effects is rudimentary and non-quantitative. We review issues of spatial and temporal scale in this paper to highlight conceptual problems relating to scale and some potential solutions. We present examples of how the spatial scale of a study influences observed patterns and their interpretation, and discuss how the size of an experimental arena influences the degree to which the dynamics of studied populations are influenced by exchange processes (immigration and emigration). The results of small-scale field experiments in streams will often be strongly influenced by the per capita exchange rates of organisms and differences in exchange rates may explain differences in the perceived effects of stream manipulations across scales. Spatial extent also influences the amount of spatial heterogeneity within a study site or arena, with important consequences for the outcome of predator-prey interactions. We suggest that changes in the availability of prey refuges may help explain why predator manipulations in streams appear to weaken as arena size increases. We also recommend that new techniques for decomposing and quantifying spatial heterogeneity be applied to characterize scale-dependent variation in freshwater systems. Lastly, we discuss the pitfalls of mismatching the temporal scale of experiments and models. Models incorporating spatial heterogeneity and the behaviour of organisms are needed to predict the short-term outcome of perturbations in streams, whereas models predicting long-term dynamics will need to integrate the impacts of episodic disturbance and all life history stages of organisms. In general, we recommend that freshwater ecologists undertake more multi-scale sampling and experimentation to examine patterns and processes at multiple scales, and make greater attempts to match the scales of their observations and experiments to the characteristic scales of the phenomena that they investigate.     

Overlapping landscapes: A persistent,but misdirected concern when collecting and analyzing ecological data

A primary focus of wildlife ecology is studying how the arrangement, quality, and distribution of habitat influence wildlife populations at multiple spatial scales. A practical limitation of conducting wildlife–habitat investigations in the field, however, is that sampling points tend to be close to one another, resulting in spatial clustering. Consequently, when ecologists seek to quantify the effects of environmental predictors surrounding their sampling points, they encounter the issue of using landscapes that are partially or completely overlapping. A presumed problem of overlapping landscapes is that data generated from these landscapes, when used as predictors in statistical modeling, might violate the assumption of independence. However, the independence of error is the critical assumption, not the independence of predictor variables. Nonetheless, many researchers strive to avoid such overlaps through sampling design or alternative analytical procedures and specialized software programs have been created to assist with this. We present theoretical arguments and empirical evidence showing that changing the amount of overlap does not alter the degree of spatial autocorrelation. Using data derived from 2 broad-scaled avian monitoring programs, we quantified the relationship between forest cover and bird abundance and occurrence at multiple landscapes ranging from 100 m to 24 km across. We found no clear evidence that increasing overlap of landscapes increased spatial autocorrelation in model residuals. Our results demonstrate that the concern of overlapping landscapes as a potential cause of violation of spatial independency among sampling units is misdirected and represents an oversimplification of the statistical and ecological issues surrounding spatial autocorrelation. Overlapping landscapes and spatial autocorrelation are separate issues in the modeling of wildlife populations and their habitats; non-overlapping landscapes do not ensure spatial independency and overlapping landscapes do not necessarily lead to greater spatial autocorrelation in model errors. © 2011 The Wildlife Society.     

A conceptual framework for the spatial analysis of functional trait diversity

Functional trait diversity is a popular tool in modern ecology, mainly used to infer assembly processes and ecosystem functioning. Patterns of functional trait diversity are shaped by ecological processes such as environmental filtering, species interactions and dispersal that are inherently spatial, and different processes may operate at different spatial scales. Adding a spatial dimension to the analysis of functional trait diversity may thus increase our ability to infer community assembly processes and to predict change in assembly processes following disturbance or land‐use change. Richness, evenness and divergence of functional traits are commonly used indices of functional trait diversity that are known to respond differently to large‐scale filters related to environmental heterogeneity and dispersal and fine‐scale filters related to species interactions (competition). Recent developments in spatial statistics make it possible to separately quantify large‐scale patterns (variation in local means) and fine‐scale patterns (variation around local means) by decomposing overall spatial autocorrelation quantified by Moran's coefficient into its positive and negative components using Moran eigenvector maps (MEM). We thus propose to identify the spatial signature of multiple ecological processes that are potentially acting at different spatial scales by contrasting positive and negative components of spatial autocorrelation for each of the three indices of functional trait diversity. We illustrate this approach with a case study from riparian plant communities, where we test the effects of disturbance on spatial patterns of functional trait diversity. The fine‐scale pattern of all three indices was increased in the disturbed versus control habitat, suggesting an increase in local scale competition and an overall increase in unexplained variance in the post‐disturbance versus control community. Further research using simulation modeling should focus on establishing the proposed link between community assembly rules and spatial patterns of functional trait diversity to maximize our ability to infer multiple processes from spatial community structure.     

Ecological application of wavelet analysis in the scaling of spatial distribution patterns of Ceratoides ewersmanniana

Ecological experiments are usually conducted on small scales, but the ecological and environmental issues are usually on large scales. Hence, there is a clear need of scaling. Namely, when we deal with patterns and processes on larger scales, a special connection needs to be established on the small scales that we are familiar with. Here we presented a wavelet analysis method that could build relationships between spatial distribution patterns on different scales. With this method, we also studied how spatial heterogeneity and distribution patterns changed with the scale. We investigated the distribution and the habitat of C. ewersmanniana in two plots (200 m × 200 m; the distance between these two plots is 15 km) at Mosuowan desert. The results demonstrated that spatial heterogeneity and distribution patterns were incorporated into larger scales when the wavelet scale varied from one (5 m) to four (20 m). However, if the wavelet scale was above five (25 m), the spatial distribution patterns varied placidly, the oscillation frequency of landforms stabilized at 110 m, and the dynamic quantity period of C. ewersmanniana stabilized at 115–125 m. We also identified signal mutation points with wavelet analysis and verified the heterogeneity degree of local space with position variance. We found that position variance decomposed the distribution patterns on large scales into small sampling plots, and the position with the largest variance also had the strongest heterogeneity. In a word, the wavelet analysis method could scale-up spatial distribution patterns and habitat heterogeneity. With this method and other methods derived from this one, such as wavelet scale, wavelet variance, position variance and extremely direct-viewing graphs, wavelet analysis could be widely applied in solving the scaling problem in ecological and environmental studies.     

不同尺度下城市景观综合指数的空间变异特征研究

在GIS与RS技术支持下,采用5 m分辨率的SPOT遥感图像数据,从城市土地利用角度,利用半变异函数对不同尺度的景观多样性、聚集度与分维数的空间变异进行了定量分析.结果表明,不同尺度下3种指数的空间变异具有相似特征,各个尺度上都具有空间依赖性,尺度越小,空间依赖性越大,空间变异的细节更显著,空间自相关性对总体变异的贡献逐渐增加,但尺度过小,有时会破坏景观内部结构.不同指数的半变异函数模型在相同尺度上差异显著,说明不同景观指数在不同尺度下的半变异函数模型不具可比性.就研究上海市内部土地利用结构而言,1 km的幅度是较合适的空间尺度.景观指数空间变异特征是尺度的函数,尺度对景观格局的影响不能忽视.景观综合指数对尺度响应的生态过程揭示了上海城市空间结构的规律性:在小尺度上的复杂无规律性,中尺度上的多中心性和大尺度上的圈层结构性,但各个尺度是相互依赖的,没有绝对界限.     

城市景观多样性的空间尺度分析——以上海市外环线以内区域为例

景观生态学研究的就是某一空间尺度范围内的景观格局与生态过程。因为景观格局与生态过程中存在的尺度多样性 ,导致尺度成为理解景观格局和生态过程相互作用的关键 ,其已经成为景观生态学的一个重要概念 ,但是由于理论和方法的限制 ,对景观生态学的尺度研究还不够 ,特别是景观格局综合性指标在不同幅度上的变化特征和效应。在 GIS与 RS技术支持下 ,采用基准分辨率为 5 m的 SPOT遥感图像作为数据源 ,对不同幅度下的城市景观多样性的空间分布格局进行了分析 ,并进一步利用半变异函数对其空间异质性进行定量描述。结论揭示 :随着空间尺度的增加 ,景观多样性程度也不断增加 ,另外多样性的空间分布格局也具有显著变化 ,由于受城市发展历史和目前城市扩展方向的影响 ,多样性在总体上是不平衡的 ,尺度越大 ,不平衡越明显 ;不同尺度下景观多样性空间格局的变化 ,与城市景观的特点和城市景观的功能息息相关 ,不过其受经济效益和社会文化效益的影响更大 ;随着尺度增加由于掩盖了更小尺度上的变异 ,导致块金效应增强 ,空间自相关部分对系统总的变异则明显下降 ;景观多样性具有尺度依赖性 ,可以说景观多样性也是尺度的函数 ,在不同的尺度上 ,结果差异显著 ,所以在景观生态学的研究中绝对不能忽略尺度对格局的影响     

Spatial scaling and transition in pneumatophore arthropod communities

Although most ecological variables are scale-dependent, few studies cover a broad range of spatial scales. Here, we consider South African mangrove pneumatophore arthropod communities (mites, crustaceans and insects), across seven spatial scales (from 10  cm to 100  km). We plot spatial autocorrelation in individual species, evaluate if resource and habitat availability determine spatial patterning, and identify the scales of community transition. Spatial autocorrelation in most ecological variables decreased with increasing spatial scale, with notable exceptions for the larger scales. Negative abundance autocorrelation was stronger at 10  km than at 100  km for common species, while the opposite was true for rare species. Spatial autocorrelation in species richness decreased from 1  m (strong positive) to 10  km (strong negative), but was not significant at the 100  km scale. These patterns reflect the patchy distribution of pneumatophores within mangrove forests, that of the forests along the coast, and the poor dispersal abilities of most of the arthropods sampled, in a highly dynamic environment. Although resource and habitat availability exhibited a similar autocorrelation pattern to that of the community, the total mass of pneumatophores did not appear to be an important determinant of community structure. Variations in the abundance of common species, as well as the restricted distribution of rare species caused assemblage structure to change gradually with increasing distance from 10 cm to 100 km, but only marginally from 10 to 100  km. We highlight the need for cross-scale studies in bridging the gap between two key ecological concepts: potential ecological niche and realized geographic range.     

生态学的时空特性(英文)

 众所周知,几乎所有的生态学特征和现象都受限于一定的时间和空间。因此,相应的科学假设和相关的生态学结论也只能基于这些特定的时空尺度范围。我们利用颇为熟知的事例,引用生态学文献中的具体实例,提纲挈领地论述了时空在生态学研究中的重要性。这些实例包括我们在长白山对云、冷杉(Picea jezoensis, Abies nephrolepis)林林冠结构的模拟、在北美应用遥感和气象方法对碳通量的估算,以及测定湿地生态系统对加温的反应等。文中所涉及的所有生态学现象,对时间和空间都有强烈的依赖性。因而, 从生态学问题的提出,到科学假设的演绎,以至试验设计和综合数据分析,都必须以时、空为前提,才不至于导致荒谬结论。     

Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM)

Aim Analyses of species distributions are complicated by various origins of spatial autocorrelation (SAC) in biogeographical data. SAC may be particularly important for invasive species distribution models (iSDMs) because biological invasions are strongly influenced by dispersal and colonization processes that typically create highly structured distribution patterns. We examined the efficacy of using a multi‐scale framework to account for different origins of SAC, and compared non‐spatial models with models that accounted for SAC at multiple levels. Location We modelled the spatial distribution of an invasive forest pathogen, Phytophthora ramorum, in western USA. Methods We applied one conventional statistical method (generalized linear model, GLM) and one nonparametric technique (maximum entropy, Maxent) to a large dataset on P. ramorum occurrence (n = 3787) to develop four types of model that included environmental variables and that either ignored spatial context or incorporated it at a broad scale using trend surface analysis, a local scale using autocovariates, or multiple scales using spatial eigenvector mapping. We evaluated model accuracies and amounts of explained spatial structure, and examined the changes in predictive power of the environmental and spatial variables. Results Accounting for different scales of SAC significantly enhanced the predictive capability of iSDMs. Dramatic improvements were observed when fine‐scale SAC was included, suggesting that local range‐confining processes are important in P. ramorum spread. The importance of environmental variables was relatively consistent across all models, but the explanatory power decreased in spatial models for factors with strong spatial structure. While accounting for SAC reduced the amount of residual autocorrelation for GLM but not for Maxent, it still improved the performance of both approaches, supporting our hypothesis that dispersal and colonization processes are important factors to consider in distribution models of biological invasions. Main conclusions Spatial autocorrelation has become a paradigm in biogeography and ecological modelling. In addition to avoiding the violation of statistical assumptions, accounting for spatial patterns at multiple scales can enhance our understanding of dynamic processes that explain ecological mechanisms of invasion and improve the predictive performance of static iSDMs.     

小波分析方法在心叶驼绒藜(Ceratoides ewersmanniana)空间格局尺度推绎研究中的应用

以古尔班通古特沙漠南缘莫索湾沙地选取相隔15km的两个200m×200m样地,以建群种心叶驼绒藜(Ceratoides ewersmanniana)及其生境地形为研究对象,应用小波分析定量研究了多尺度上空间格局的推绎以及空间异质性、空间格局依赖于尺度的变化关系。研究发现：小波分析尺度由1(5m)变化到4(20m)时,两个样地小尺度上的异质性和格局被合并到更大的尺度上,当小波分析的尺度大于等于5(25m)时,两个样地的格局变化平稳,对应地形（丘顶、丘坡、丘底）的基频稳定在110m左右,心叶驼绒藜的数量动态变化周期稳定在115～125m之间。结果表明：小波分析对信号整体特征的提取作用实现了小尺度上的信息到大尺度上的聚合。结合小波分析对信号突变点的检测,利用位置方差检验局部空间异质性程度,发现位置方差将大尺度上的格局分解到每个取样小样方,位置方差最大的地点对应的异质性也最强,实现了大尺度上的信息到小尺度上的分解。总结认为应用小波分析可以实现对空间格局的尺度推绎,具有对植被、环境的分布格局以及异质性有双重度量作用,由小波系数以及由其衍生的小波方差、位置方差来实现这种度量,图形表现直观,优越性明显。     

Spatial correlations at different spatial scales are themselves highly correlated in isolation by distance processes

Although many properties of spatial autocorrelation statistics are well characterized, virtually nothing is known about possible correlations among values at different spatial scales, which ultimately would influence how inferences about spatial genetics are made at multiple spatial scales. This article reports the results of stochastic space-time simulations of isolation by distance processes, having a very wide range of amounts of dispersal for plants or animals, and analyses of the correlations among Moran's I-statistics for different mutually exclusive distance classes. In general, the stochastic correlations are extremely large (>0.90); however, the correlations bear a complex relationship with level of dispersal, spatial scale and spatial lag between distance classes. The correlations are so large that any existing or conceived statistical method that employs more than one distance class (or spatial scale) should not ignore them. This result also suggests that gains in statistical power via increasing sample size are limited, and that increasing numbers of assayed loci generally should be preferred. To the extent that sampling error for real data sets can be treated as white noise, it should be possible to account for stochastic correlations in formulating more precise statistical methods. Further, while the current results are for isolation by distance processes, they provide some guidance for some more complex stochastic space-time processes of landscape genetics. Moreover, the results hold for several popular measures other than Moran's I. In addition, in the results, the signal to noise ratios strongly decreased with distance, which also has several implications for optimal statistical methods using correlations at multiple spatial scales.     

Testing for spatial autocorrelation in ecological studies

We describe a statistical method appropriate for the analysis of spatial autocorrelation in data varying in time as well as space. In particular, the technique was developed lo address the issue of geographic synchrony in ecological variables that may change markedly from year to year such as population density of animals or seed production of trees. The method yields 'modified correlograms" that test for significant autocorrelation between sites located within any given range of distances apart. This technique facilitates detecting and understanding spatial processes m a variety of ecological phenomena, including testing the plausibility of causational hypotheses using cross-correlational analyses. Several examples are discussed, including population densities of squirrels in Finland, winter densities of two hawk species in California, and acorn production and radial growth by individual blue oak Quercus douglasii trees in central coastal California.     

The inselberg flora of Atlantic Central Africa. I. Determinants of species assemblages

Aims To identify the relative contributions of environmental determinism, dispersal limitation and historical factors in the spatial structure of the floristic data of inselbergs at the local and regional scales, and to test if the extent of species spatial aggregation is related to dispersal abilities. Location Rain forest inselbergs of Equatorial Guinea, northern Gabon and southern Cameroon (western central Africa). Methods We use phytosociological relevés and herbarium collections obtained from 27 inselbergs using a stratified sampling scheme considering six plant formations. Data analysis focused on Rubiaceae, Orchidaceae, Melastomataceae, Poaceae, Commelinaceae, Acanthaceae, Begoniaceae and Pteridophytes. Data were investigated using ordination methods (detrended correspondence analysis, DCA; canonical correspondence analysis, CCA), Sørensen's coefficient of similarity and spatial autocorrelation statistics. Comparisons were made at the local and regional scales using ordinations of life‐form spectra and ordinations of species data. Results At the local scale, the forest‐inselberg ecotone is the main gradient structuring the floristic data. At the regional scale, this is still the main gradient in the ordination of life‐form spectra, but other factors become predominant in analyses of species assemblages. CCA identified three environmental variables explaining a significant part of the variation in floristic data. Spatial autocorrelation analyses showed that both the flora and the environmental factors are spatially autocorrelated: the similarity of species compositions within plant formations decreasing approximately linearly with the logarithm of the spatial distance. The extent of species distribution was correlated with their a priori dispersal abilities as assessed by their diaspore types. Main conclusions At a local scale, species composition is best explained by a continuous cline of edaphic conditions along the forest‐inselberg ecotone, generating a wide array of ecological niches. At a regional scale, these ecological niches are occupied by different species depending on the available local species pool. These subregional species pools probably result from varying environmental conditions, dispersal limitation and the history of past vegetation changes due to climatic fluctuations.     

Scale dependence of vegetation-environment correlations: A case study of a North Carolina piedmont woodland

Abstract. Vegetation and its correlation with environment has been traditionally studied at a single scale of observation. If different ecological processes are dominant at different spatial and temporal scales, the results obtained from such observations will be specific to the single scale of observation employed and will lack generality. Consequently, it is important to assess whether the processes that determine community structure and function are similar at different scales, or whether, how rapidly, and under what circumstances the dominant processes change with scale of observation. Indeed, early work by Greig-Smith and associates (Greig-Smith 1952; Austin & Greig-Smith 1968; see Greig-Smith 1979; Kershaw & Looney 1985; Austin & Nicholls 1988) suggested that plant-plant interactions are typically important at small scales, but that the physical environment dominates at large scales. Using a gridded and mapped 6.6 ha portion of the Duke Forest on the North Carolina piedmont for a case study, we examined the importance of scale in vegetation studies by testing four hypotheses. First, we hypothesized that the correlation between vegetation composition and environment should increase with increasing grain (quadrat) size. Our results support this hypothesis. Second, we hypothesized that the environmental factors most highly correlated with species composition should be similar at all grain sizes within the 6.6-ha study area, and should be among the environmental factors strongly correlated with species composition over the much larger extent of the ca. 3500 ha Duke Forest. Our data are not consistent with either portion of this hypothesis. Third, we hypothesized that at the smaller grain sizes employed in this study (< 256 m²), the composition of the tree canopy should contribute significantly to the vegetation pattern in the under-story. Our results do not support this hypothesis. Finally, we predicted that with increased extent of sampling, the correlation between environment and vegetation should increase. Our data suggest the opposite may be true. This study confirms that results of vegetation analyses can depend greatly on the grain and extent of the samples employed. Whenever possible, sampling should include a variety of grain sizes and a carefully selected sample extent so as to ensure that the results obtained are robust. Application of the methods used here to a variety of vegetation types could lead to a better understanding of whether different ecological processes typically dominate at different spatial scales.     

基于TM影像的景观空间自相关分析——以北京昌平区为例

格局与尺度之间的关系是景观生态学的核心研究内容。景观格局发生在不同的尺度 ,而尺度又影响格局的研究 ,因而 ,在景观生态学研究中应用多种量化研究方法于一系列尺度来确定和特征化空间格局研究 ,并探求空间格局与生态学功能和生态学过程之间的关系是非常必要的。以北京昌平区为例 ,从 TM影像中选取 5个具有突出自然和社会经济背景差异的景观 ,即林地景观、农田景观、都市边缘景观、卫星城景观和灌丛景观为研究对象 ,基于归一化植被指数 (N DVI) ,采用常用空间自相关指数 ,即 Moran的 I系数和 Geary的 c系数进行一系列的空间自相关分析 ,旨在阐明 :变化的空间粒度如何影响空间分析 ?以及空间分析如何响应划区效应 ?此外 ,基于 N DVI和数字高程模型 (DEM)也探讨了对于不同的数据类型 ,格局的尺度依赖性如何变化。研究结果表明 :空间粒度的变化对于景观分析有着显著的影响 ,随着空间粒度的增加 ,空间自相关均呈下降趋势 ;不同景观类型对于空间粒度的变化有着不同的响应 ,人为干扰较多的景观具有较低的空间自相关 ,但对空间粒度的变化表现出较强的敏感性 ;对于不同的数据类型 ,格局分析对空间粒度变化的响应是不同的     

Investigating the causes of fish community change in the Dahan River (Taiwan) using an autecology matrix

Recent work in river restoration and water resources management has seen the need to change the focus of analysis from reach to watershed scales to better define causes of watershed impairment. However, comprehensive investigations at a watershed scale are hindered by difficulties in using reach data that was collected for analysis at small spatial and short temporal scales. This is especially true for ecological and biological data. The approach assembles assessment and monitoring data and uses an autecology matrix to identify the changes in environmental and ecological conditions that may be associated with community change over spatial and temporal scales appropriate for ecosystem analysis in watersheds. The analysis uses a weight-of-evidence approach based on the percent of the community associated with a matrix factor. We have used the autecology matrix to examine historical fish community data from the Dahan River, Taiwan. The results show that the method provides an improved understanding of historical influences on fish community structure and supports a process-based analysis of community change over watershed scales and historic time periods. Further the method helps identify habitat requirements for the fish communities at each sampling site, supporting management and ecological restoration objectives.     

不同尺度苔藓结皮土壤性状的空间分布特征

苔藓结皮是生物结皮的高级阶段,它在防风固沙、改善土壤水分、养分等方面具有十分重要的生态作用,但前期研究多是对结皮区和无结皮覆盖区土壤性状的比较,对结皮斑块内土壤理化因子的空间分布情况尚不明确。运用经典统计学,地统计学以及克里格插值法探讨了土壤水分、有机质、全氮、全磷、总盐在样方和斑块两个尺度的空间变异特征,旨在阐明不同尺度藓类结皮土壤空间异质性的强度,明确不同尺度土壤各性状合适的取样距离。研究结果表明藓类结皮土壤性状在两种尺度均表现出中等程度的变异,随着取样尺度减小,自相关性在空间异质性中占的比重增加,结构因素的影响增强,随机因素的影响减弱。样方尺度的空间异质性大于斑块尺度异质性,且土壤性状的变程较大,采样时可适当增大取样间距;两种尺度下5个土壤性状中,全磷的变程最小,实际取样时应适当缩小取样距离,总盐变程大,自相关程度低,因而取样间距可适当放大。斑块尺度,藓类结皮土壤性状由边缘向中心递增(磷递减)的现象与斑块的边缘效应有关。