放牧和刈割条件下草山草坡群落空间异质性分析

采用变异矩分析和分形方法,研究了草山草坡群落在放牧和刈割条件下的空间异质性及空间自相关性,结果表明,群落空间格局有尺度依赖性,刈割条件下空物异质性及空间相关性弱,多样性梯度即β多样性小,放牧消除地形引起的样地差异,因而使空间异质性简单化。     

Predicting forest grouse distribution taking account of spatial autocorrelation

Spatially explicit, multi-scale models for predictions of species potential distribution can be useful tools for integrating biodiversity considerations in planning and strategic environmental assessment. In such models, the occurrences of focal species are related to habitat and landscape variables, which in urbanising areas should also include effects of urban disturbances. Moreover, the accuracy of the spatial predictive models may be affected by spatial autocorrelation, which means that a part of the variance is explained by neighbouring values. The aim of this study was to explore the effects of habitat and disturbance patterns on the distribution of two forest grouse species, Tetrao urogallus and Bonasa bonasia, and to detect and model the effects of spatial autocorrelation. The distribution of the two species could be explained in terms of reduction of a main predator, habitat quality, quantity and connectivity, including urban disturbances. The residuals of the initial regressions showed positive spatial autocorrelation that could be quantified by using a spatial probit model. The application of the spatial probit model revealed strongly significant spatial dependencies for both species. Furthermore, the model fit could be increased for T. urogallus by applying this model. The results implied that both species distributions might be affected by both reactions to the underlying land-use pattern, but also by interaction with neighbours. The use of the spatial probit model is a way to incorporate spatial interactions that otherwise cannot be captured by the independent variables.     

Multi-proxy evidence for competition between savanna woody species

Coexistence of trees and grasses in savannas should be possible if competition between the woody and the grassy components is less intense than the competition within each component. Although several studies have investigated competition between trees and grasses, little is known about tree–tree interactions. We used a multi-proxy approach to examine the spatial pattern of Acacia mellifera and other savanna woody species in a semi-arid savanna in South Africa. Spatial analysis of the point patterns of young and reproductively mature shrubs detected decreasing aggregation with size/age over all spatial scales. This indicated the prevalence of competition although the overall spatial shrub pattern was aggregated. In contrast to point pattern statistics that detect changes only when competition has led to the death of the inferior competitor, we also applied methods identifying the competitive effect on sizes of individual trees. Competition should lead to a negative spatial autocorrelation in size, which we observed in half of the studied cases. Quantile regressions show that nearest-neighbour distance increased steeply with combined size of the target shrub and its neighbours indicating strong competitive effects. The medians of the distributions of maximum root lengths of A. mellifera, of the scale of regular patterns, and of negative autocorrelations were not significantly different, suggesting that overlapping root systems mediate competitive interactions. A competitor removal experiment did not lead to increased shrub sizes, which may be due to the limited duration of the experiment. From the nearest neighbour and autocorrelation analyses, we conclude that competition had a strong impact on growth rates of savanna woody species. Competition-induced mortality only becomes obvious when analysing the shift towards less aggregated spatial patterns when shrubs become reproductively mature. As the overall clustered spatial pattern masks the perceptible effect of competition, a time component should always be included in spatial pattern-based inference of competition.     

Integrating ecological features of species in spatial pattern analysis of a plant community

Objective: This empirical study was designed to explore the role of ecological features of species in the spatial patterning of a grassland community. Location: Banks of the river Rhône in France. Material and Methods: First, we explored the spatial pattern of 29 species recorded in the community using spatial autocorrelation analysis of species cover values. Second, we then explored the relationship between the patterns found and a set of life attributes that characterized the ecological features of species for resource foraging or dispersion. Finally, we explored the spatial relationship of groups of species that shared the same ecological features using cross‐correlation analysis. Results: We found a significant relationship between the spatial pattern and life attributes of the species highlighting three groups of species: (1) species characterized as competitors, reproducing by runner clonal organs and forming large, dense patches; (2) species characterized as competitive‐rud‐erals, dispersing exclusively by seed production and forming small periodic patches; and (3) species classified as CSR, characterized by rosette morphology and short rhizomes as clonal organs without any significant spatial autocorrelation. Spatial segregation was found between group 1 and group 2 up to 14 m; no significant cross‐correlation between groups 1 and 3 between 0 and 3.5 m, and association between groups 2 and 3 up to 14 m. Conclusions: These results helped to understand how species attributes (relative to stature or dispersion abilities); external factors (such as disturbance) and biotic processes (competition) interplay in structuring the plant community under study in space.     

Spatial associations and patterns of perennial vegetation ina semi-arid steppe: a multivariate geostatistics approach

We investigated the spatial patterns of perennial species (Stipa tenacissima, Anthyllis cytisoidesGlobularia alypum, Brachypodium retusum and chamaephytes) in a 50 m × 50 m semi-arid steppe by using the combination of a linear model of coregionalization (LMC) and sampling units of varying size (1.25 m × 1.25 m, 2.5 m × 2.5 m, and 5 m × 5 m). The data-adjusted LMC showed the patchy structure of the vegetation, which was especially evident with the highest resolution grid. It also detected a periodic pattern in the distribution of S. tenacissima, as well as autocorrelation at two spatial scales for A. cytisoides and G. alypum. The latter species was negatively associated with the other species at both short and long distances. These negative associations were consistent for all sampling grids and suggest the presence of interference between G. alypum and the rest of the evaluated species. Despite species-specific differences, the LMC was fitted satisfactorily to all of them. This suggests a common variation pattern for all the species, which may be caused by an underlying environmental property driving the patterns of all the species or, alternatively, by the dominance of some species’ spatial pattern, or another kind of species association, over the rest. The spatial patterns found were profoundly affected by the observational scale. Our results reveal that the multivariate geostatistical approach introduced in this paper is a suitable technique for the spatial analysis of semi-arid plant communities. It allows plant ecologists to evaluate if the species forming the plant community of interest share a common spatial pattern, and to assess the spatial covariation between the species forming a plant community at different spatial scales independently.     

毛乌素沙地油蒿种群点格局分析

油蒿(Artemisia ordosica)是我国北方农牧交错带的重要固沙植物,研究其种群格局对理解种群生态过程和改善流沙治理技术具有重要意义。点格局分析法是20世纪末发展起来的多尺度空间格局分析方法。通过研究油蒿种群的点格局,发现油蒿种群的空间分布格局和空间关联性同空间尺度、植株形体大小以及生境3种因素有密切联系。在较小的空间尺度上,油蒿种群倾向于非随机分布(集群分布比均匀分布常见),个体间有较强的空间关联(正关联比负关联常见);当空间尺度大于临界值后,油蒿种群倾向于服从随机分布,同时种群的空间关联性减弱。幼小油蒿植株具有明显的集群分布趋势,高大植株则表现出聚集强度的降低趋势;形体大小的差异越大,植株间的正关联关系越弱,或者负关联关系越强。与固定沙地相比,半固定沙地油蒿种群的集群分布现象更加明显,同时种群的空间正关联关系更强。研究结果表明,当通过移栽油蒿成体治理流动沙地时,应尽量将其栽种成集群分布而非均匀分布的形式,以提高植株成活率。     

Linking small desert mammal distribution to habitat structure in a protected and grazed landscape of the Monte, Argentina

Interpretation of landscape patterns from the perspective of different species allows knowing the way in which they perceive landscape, and how their perception varies with scale. We examined distribution of four small mammal species at different scales over a landscape including protected and grazed areas, and associated species distribution with landscape structure. The study was conducted in the central Monte Desert (Reserve of Ñacuñán). Trap grids were set in both areas at two scales, varying their grain and extent. To determine whether spatial patterns are random, clumped or regular, we used a point pattern analysis. Logistic regressions were performed to relate the presence-absence of small mammals to environmental variables. Intensity of the point pattern was not constant, either in the Reserve or the grazed area. Small mammal abundance exhibited a heterogeneous distribution, and the existence of a first-order effect was detected for all species. No second-order effects were detected, the point pattern was random for all species in both areas. Both areas were differently perceived by rodent species. Habitat structure in both conditions and its variations with scale appear to be important factors affecting distribution patterns.     

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     

Evaluating spatial autocorrelation and depletion in pitfall-trap studies of environmental gradients

Studies of environmental gradients like edge effects commonly employ designs where samples are collected at unequal distances within transects. This approach risks confounding species patterns caused by the environmental gradient with patterns resulting from the spatial arrangement of the sampling scheme. Spatial autocorrelation and depletion (reduced catch) have the potential to influence pitfall-trap collections of invertebrates. Readily available control data from a study of edge and riparian effects on forest litter beetles was used to assess autocorrelation and depletion effects. Data from control transects distant from the treatment transects located at habitat edges and streams were screened to determine whether the study design (pitfall traps at varying distances within transects) was imposing patterns on the data attributable to differential autocorrelation or depletion. Autocorrelation in species composition and assemblage structure was not detected within the 99 m transects. The abundance and species richness of beetles were not lower where traps were in closer proximity, indicating that the transect design was not causing measurable depletion or resulting in differential trap catch. These findings indicate that spatial autocorrelation and depletion are unlikely to impair further analyses of edge and riparian effects on litter beetles.     

崇明东滩越冬鸟类在养殖塘的空间分布

鸟类对空间的利用直接反映对栖息地的选择。通过对崇明东滩养殖塘越冬鸟类群落调查,运用空间自相关检验、空间插值分析和景观格局分析,研究越冬鸟类在养殖塘人工湿地的空间分布格局。结果显示:雁鸭类和鸻鹬类在养殖塘分布特征显著不同。雁鸭类呈现显著的空间自相关,为聚集分布,且丰富度和多度分布较为一致;而鸻鹬类在养殖塘以随机分布为主,丰富度显示一定的聚集分布。养殖塘已经成为崇明东滩越冬鸟类重要的栖息地,不同生态类群栖息地选择存在差异:芦苇植被发育好,水域面积较小且水深较深,不同斑块以聚集为特征的养殖塘是雁鸭类的适宜生境;鸻鹬类则偏好一定芦苇植被、水域面积大且水深较浅的养殖塘。因此在保护区土地利用和管理时应考虑不同生态类群对栖息地选择的差异。     

Incorporating spatial autocorrelation may invert observed patterns

Though still often neglected, spatial autocorrelation can be a serious issue in ecology because the presence of spatial autocorrelation may alter the parameter estimates and error probabilities of linear models. Here I re-analysed data from a previous study on the relationship between plant species richness and environmental correlates in Germany. While there was a positive relationship between native plant species richness and an altitudinal gradient when ignoring the presence of spatial autocorrelation, the use of a spatial simultaneous liner error model revealed a negative relationship. This most dramatic effect where the observed pattern was inverted may be explained by the environmental situation in Germany. There the highest altitudes are in the south and the lowlands in the north that result in some locally or regionally inverted patterns of the large-scale environmental gradients from the equator to the north. This study therefore shows the necessity to consider spatial autocorrelation in spatial analyses.     

Spatial autocorrelation and the analysis of invasion processes from distribution data: a study with the crayfish <Emphasis Type="Italic">Procambarus clarkii</Emphasis>

Complex spatial dynamics are frequent in invasive species; analyzing distribution patterns can help to understand the mechanisms driving invasions. We used different spatial regression techniques to evaluate processes determining the invasion of the red swamp crayfish Procambarus clarkii. We evaluated four a priori hypotheses on processes that may determine crayfish invasion: landscape alteration, connectivity, wetland suitability for abiotic and biotic features. We assessed the distribution of P. clarkii in 119 waterbodies in a recently invaded area. We used spatially explicit statistical techniques (spatial eigenvector mapping, generalized additive models, Bayesian intrinsic conditional autoregressive models) within an information-theoretic framework to assess the support of hypotheses; we also analyzed the pattern of spatial autocorrelation of data, model residuals, and eigenvectors. We found strong agreement between the results of spatial eigenvector mapping and Bayesian autoregressive models. Procambarus clarkii was significantly associated with the largest, permanent wetlands. Additive models suggested also association with human-dominated landscapes, but tended to overfit data. The results indicate that abiotic wetlands features and landscape alteration are major drivers of the species’ distribution. Species distribution data, residuals of ordinary least squares regression, and spatial eigenvectors all showed positive and significant spatial autocorrelation at distances up to 2,500 m; this may be caused by the dispersal ability of the species. Our analyses help to understand the processes determining the invasion and to identify the areas most at risk where screening and early management efforts can be focused. The comparison of multiple spatial techniques allows a robust assessment of factors determining complex distribution patterns.     

Habitat distribution models,spatial autocorrelation,functional traits and dispersal capacity of alpine plant species

Abstract. We evaluate the potential influence of disturbance on the predictability of alpine plant species distribution from equilibrium‐based habitat distribution models. Firstly, abundance data of 71 plant species were correlated with a comprehensive set of environmental variables using ordinal regression models. Subsequently, the residual spatial autocorrelation (at distances of 40 to 320 m) in these models was explored. The additional amount of variance explained by spatial structuring was compared with a set of functional traits assumed to confer advantages in disturbed or undisturbed habitats. We found significant residual spatial autocorrelation in the habitat models of most of the species that were analysed. The amount of this autocorrelation was positively correlated with the dispersal capacity of the species, levelling off with increasing spatial scale. Both trends indicate that dispersal and colonization processes, whose frequency is enhanced by disturbance, influence the distribution of many alpine plant species. Since habitat distribution models commonly ignore such spatial processes they miss an important driver of local‐ to landscape‐scale plant distribution.     

Below-ground spatial pattern of rhizomes in a grassland community and its relevance to above-ground spatial pattern

Studies of spatial patterns in grassland plant communities have focused on above-ground patterns, ignoring the fact that in clonal plant communities, such as those found in grasslands, above-ground spatial structure must reflect below-ground horizontal growth. The present study examines, at both a fine and a coarse spatial scale, relationships between rhizome and ramet distribution. At the coarse scale, the dominance of species differed between above- and below-ground; some species dominated only above- or below-ground, and others dominated in both layers. At the fine scale, a single species' ramet aggregation above-ground significantly differed from its rhizome aggregation below-ground, for many species. Even for a given species, quantitative relationships between above- and below-ground dominance varied among localities. The variation in spatial pattern among species can be explained by attributes of clonal growth form, including rhizome size, rhizome origin and pattern of above-ground ramet aggregation. Using these parameters of clonal growth, four major space occupation patterns were described for mountain grassland species. For species with a high abundance of evenly distributed rhizomes, ramets either i) reflect rhizome distribution, or ii) do not fully reflect rhizome distribution, but rather are spatially aggregated, and rhizomes are typically developed below-ground. For species with a low abundance of rhizomes, ramets either iii) reflect rhizome distribution and rhizomes are typically of above-ground origin, or iv) do not reflect rhizome distribution and are aggregated only at the growing tips of rhizomes. Spatial correlation above-ground among species was the same as below-ground for some pairs of species but was significantly different for other pairs. This revised version was published online in June 2006 with corrections to the Cover Date.     

Ecological biogeography of southern ocean islands: the importance of considering spatial issues

Understanding patterns of among-island variation in species richness has long been an important question in ecology and biogeography. However, despite the clear spatial nature of the data used for such investigations, the spatial distribution of the different sampled locations is rarely explicitly considered, which may be critical for statistical and biological reasons. In a recent study, Chown et al. (1998) investigated the relationships between species richness of different indigenous and introduced taxonomic groups and a variety of variables characterizing Southern Ocean islands, and here, we use these data to address spatial issues. As predicted, we found spatial autocorrelation in species richness for terrestrial taxa with high dispersal ability or for terrestrial taxa that had time to disperse locally (introduced land birds and indigenous taxa) but not for taxa that had low opportunity to disperse to nearby islands (introduced plants, insects, and mammals), which suggests that colonization from nearby islands has played an important role in shaping present-day patterns of among-island variation in species richness. Interestingly, in several cases, the estimated effect of variables changed when spatial covariance was incorporated. Moreover, the absence of autocorrelation of some variables allowed us to confirm some important results of Chown et al. (1998), notably those involving the potential impact of human presence on the biodiversity of these islands. Overall, our results illustrate the importance of considering spatial structures in ecological studies. This is notably the case when dispersal processes can be expected to explain some of the observed patterns.     

Covariances among join-count spatial autocorrelation measures

Spatial distributions of biological variables are often well-characterized with pairwise measures of spatial autocorrelation. In this article, the probability theory for products and covariances of join-count spatial autocorrelation measures are developed for spatial distributions of multiple nominal (e.g. species or genotypes) types. This more fully describes the joint distributions of pairwise measures in spatial distributions of multiple (i.e. more than two) types. An example is given on how the covariances can be used for finding standard errors of weighted averages of join-counts in spatial autocorrelation analysis of more than two types, as is typical for genetic data for multiallelic loci.     

Spatio-temporal dynamics of <Emphasis Type="Italic">Scotinophara lurida</Emphasis> (Hemiptera: Pentatomidae) in rice fields

Studying the spatial pattern of insect pests and the temporal stability of their patterns is important in understanding underlying ecological mechanisms and in developing pest management programs in cultivated crop systems. To elucidate the spatio-temporal pattern of the black rice bug, Scotinophara lurida, in rice fields, samplings were conducted in two rice fields over 2 years. Using spatial analysis by distance indices, the spatial pattern of each developmental stage of S. lurida and their temporal stability of the spatial pattern were identified. Most of the I _a (the index of aggregation) values for overwintered adults and eggs of S. lurda were close to 1, indicating random distribution pattern while nymphs and new adults mainly had I _a values >1, indicating an aggregated distribution pattern. According to spatial association analysis between successive samples using X (the index of spatial association), the spatial pattern of S. lurida showed strong temporal stability throughout the season. Also, there was strong association between the spatial patterns of developmental stages, indicating the great effect of the spatial pattern of the previous developmental stage on that of later developmental stage. Factors influencing the spatial pattern and spatial stability of S. lurida are discussed.     

Size of environmental grain and resource matching

For most animals their foraging environment consists of a patch network. In random environments there are no spatial autocorrelation at all, while in fine-grained systems positive autocorrelations flip to negative ones and back again against distance. With increasing grain size the turnover rate of spatial autocorrelation slows down. Using a cellular automaton with foragers having limited information about their feeding environment we examined how well consumer numbers matched resource availability, also known as the ideal free distribution. The match is the better the smaller the size of the environmental grain. This is somewhat contrary to the observation that in large-grained environments the spatial autocorrelation is high and positive over long distances. In such an environment foragers, by knowing a limited surrounding, should in fact know a much larger area because of the spatially autocorrelated resource pattern. Yet, when foragers have limited knowledge, we observed that the degree of undermatching (i.e., more individuals in less productive patches than expected) increases with increasing grain size.     

Spatial patterns of plant association in grazed and ungrazed shrublands in the semi‐arid Karoo,South Africa

Abstract. The investigation of vegetation pattern and plant association by spatial statistics has become increasingly popular among plant ecologists. Recently, Individual‐centered analysis (ICA) has been introduced as a new tool for analysis of multi‐species co‐occurrence patterns. We tested this new technique by applying it to spatial data from grazed and ungrazed shrub communities in the semi‐arid Great Karoo, South Africa. There were substantial but complex and scale‐dependent differences in pattern between grazed and ungrazed vegetation. Unpalatable species that increase in abundance in grazed vegetation possibly play a key role in the change of vegetation pattern. At small scales we found indications of aggregation (< 30 cm) at the ungrazed, but of repulsion (30 – 40 cm) at the grazed site. An additional non‐random pattern at 60 – 170 cm at the grazed site was probably due to the clumped distributions of some species on broader scales. We show that the interpretability of ICA results is improved when the actual observed and expected frequencies of species combinations are added to the program output. The main strength of ICA is that it has the potential to detect association patterns that involve more than two species.     

红松老龄林主要树种的空间异质性特征与比较的定量研究

 空间异质性研究对认识不同尺度上的生态系统结构、功能和过程具有重要的理论意义。采用地统计学理论和方法,通过空间异质性特征和比较研究了我国东北红松老龄林中主要树种,即红松(Pinus koraiensis)、云杉(Picea koraiensis)、冷杉(Abies nephrolepis)、枫桦(Betula costata)和紫椴(Tilia amurensis)的空间异质性程度、空间异质性组成、尺度和格局问题。结果表明：红松老龄林中主要树种虽然生长在同一林分中,但是它们的空间异质性与格局具有明显的差异。红松的空间异质性程度最高,分数维较低,其空间异质性主要由空间自相关部分组成,在450m的尺度内,具有明显的空间格局。枫桦的空间异质性程度最低,分数维最大,空间异质性主要由随机部分组成,在大于100m的尺度上不存在明显的空间格局。云杉和冷杉由于生长的立地相似,因此具有相似的空间异质性特征和格局。紫椴是红松最重要的伴生树种,其空间异质性程度、空间异质性组成、尺度和格局等方面与红松接近。红松老龄林中主要树种空间异质性与格局在一定尺度上的差异,反映这些树种在生态系统中具有不同的生态学功能和过程。