土地利用变化影响下洞庭湖地区生态系统服务价值的时空演变

对洞庭湖地区多年遥感影像进行解译,结合其经济发展统计数据,在地理信息系统技术支持下,估算各土地利用类型的生态价值系数,并运用空间自相关、高地聚类及热点分析等空间地统计分析方法,探究洞庭湖地区生态系统服务价值(ESV)的时空变化特征.结果表明: 2000—2013年间,洞庭湖地区的ESV小幅上升,由3868.39亿元上升至3886.62亿元,湿地面积的增加是ESV上升的主要原因;洞庭湖地区的ESV高值区与低值区相互溶解渗透,次高值区对高值及低值区呈包围态势;洞庭湖地区的ESV及其动态演化表现出明显的空间自相关与高低值聚集现象,但其自相关与聚集程度趋于减弱;研究期间洞庭湖地区的ESV热点区重心主要向西稍偏北方向移动,其轨迹大体上呈 “S”型态势,迁移速度缓慢但具有明显的阶段性,经历了 “剧烈—缓慢—剧烈”的变化阶段.     

Improved abundance prediction from presence–absence data

Aim   Many ecological surveys record only the presence or absence of species in the cells of a rectangular grid. Ecologists have investigated methods for using these data to predict the total abundance of a species from the number of grid cells in which the species is present. Our aim is to improve such predictions by taking account of the spatial pattern of occupied cells, in addition to the number of occupied cells.
Innovation   We extend existing prediction models to include a spatial clustering variable. The extended models can be viewed as combining two macroecological regularities, the abundance–occupancy regularity and a spatial clustering regularity. The models are estimated using data from five tropical forest censuses, including three Panamanian censuses (4, 6 and 50 ha), one Costa Rican census (16 ha) and one Puerto Rican census (16 ha). A serpentine grassland census (8 × 8 m) from northern California is also studied.
Main conclusions   Taking account of the spatial clustering of occupied cells improves abundance prediction from presence–absence data, reducing the mean square error of log-predictions by roughly 54% relative to a benchmark Poisson predictor and by roughly 34% relative to current prediction methods. The results have high statistical significance.     

Spatial analysis of the distribution of leprosy in the State of Ceará, Northeast Brazil

The aim of this study was to describe spatial patterns of the distribution of leprosy and to investigate spatial clustering of incidence rates in the state of Ceará, Northeast Brazil. The average incidence rate of leprosy for the period of 1991 to 1999 was calculated for each municipality of Ceará. Maps were used to describe the spatial distribution of the disease, and spatial statistics were applied to explore large- and small-scale variations of incidence rates. Three regions were identified in which the incidence of leprosy was particularly high. A spatial gradient in the incidence rates was identified, with a tendency of high rates to be concentrated on the North-South axis in the middle region of the state. Moran's I statistic indicated that a significant spatial autocorrelation also existed. The spatial distribution of leprosy in Ceará is heterogeneous. The reasons for spatial clustering of disease rates are not known, but might be related to an heterogeneous distribution of other factors such as crowding, social inequality, and environmental characteristics which by themselves determine the transmission of Mycobacterium leprae.     

小兴安岭阔叶红松林粗木质残体空间分布的点格局分析

采用点格局分析方法对小兴安岭典型阔叶红松林9hm2(300m×300m)固定样地内粗木质残体(CWD)的空间分布格局进行了研究。结果表明:固定样地内CWD的总密度为368.8株/hm2,径级结构呈现明显的正态分布,各径级密度差别较大。花楷槭(27.8株/hm2)和枫桦(26.1株/hm2)是阔叶CWD的主要组成树种。红松(41.6株/hm2)是针叶CWD的主要组成树种,针叶树种CWD的数量随着径级的增大而增加,呈典型的J型分布。在150m的空间尺度内,CWD总体在较小尺度上表现为集群分布,在40m尺度上聚集强度最大(0.40),随着尺度的增加,CWD趋于均匀分布。不同径级组在不同规模尺度聚集,随着径级的增加,聚集强度呈下降趋势。不同物种CWD在各径级下的空间分布格局有所不同,具体表现为集群分布和由集群分布向随机分布发展两种形式。不同存在形式的CWD在研究尺度内随着尺度的增加,由集群分布向随机分布发展。随着CWD腐烂等级的增高,其聚集强度和聚集尺度均增大。CWD的空间分布格局是阔叶红松林群落与其自然环境长期作用的结果,从某种程度上也反映了该林型天然更新的格局和机制。     

Characterization and comparison of protein structures. Part I-characterization.

The quantitative criteria characterizing the regularity of Calpha-backbones in the protein structures are presented. A technique is based on the Fourier remapping of the Cartesian coordinates for the Calpha-chain. The Fourier spectra identify the hidden periodicities and symmetries in protein structures, while the integral regularity is assessed via the spectral structural entropies. The formal unification of digitizing and the similarities in statistics for the random counterparts allow study of the direct correlations between the distribution of physico-chemical characteristics along the amino acid sequence and the spatial conformation of the polypeptide chain. The significant correlations are found for both hydrophobicity and side-chain volumes, though, as expected, the effects for hydrophobicity turn out essentially stronger. A scheme is illustrated by the set of 120 protein structures comprising the representatives from the main superfamilies and superfolds.     

Testing for a pulse in seasonal event data

Most tests for seasonal clustering are sensitive to the peak in incidence in some season (90-day interval) followed by a trough 6 months later. We present two statistics sensitive to a relatively sharp increase in disease incidence for a season superimposed on a constant incidence over the entire year: the ratchet scan, based on the maximum number of events in k consecutive months, and the continuous circular scan, the maximum number of events in d consecutive days. We tabulate the tail distribution of the ratchet scan for N less than or equal to 35 and k = 2 and 3, and find the asymptotic distribution which is applicable for N greater than or equal to 50 when k = 2 or 3. We evaluate an approximation for the distribution of the continuous scan. The statistics are applied to investigate seasonal clustering of adolescent suicide in the United States. An illustration is given of the application of the circular scan, and a small simulation study and the example are used to compare the statistics with others proposed in the literature.     

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set–proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.     

Temporal clustering of tropical cyclones on the Great Barrier Reef and its ecological importance

Tropical cyclones have been a major cause of reef coral decline during recent decades, including on the Great Barrier Reef (GBR). While cyclones are a natural element of the disturbance regime of coral reefs, the role of temporal clustering has previously been overlooked. Here, we examine the consequences of different types of cyclone temporal distributions (clustered, stochastic or regular) on reef ecosystems. We subdivided the GBR into 14 adjoining regions, each spanning roughly 300 km, and quantified both the rate and clustering of cyclones using dispersion statistics. To interpret the consequences of such cyclone variability for coral reef health, we used a model of observed coral population dynamics. Results showed that clustering occurs on the margins of the cyclone belt, being strongest in the southern reefs and the far northern GBR, which also has the lowest cyclone rate. In the central GBR, where rates were greatest, cyclones had a relatively regular temporal pattern. Modelled dynamics of the dominant coral genus, Acropora, suggest that the long-term average cover might be more than 13 % greater (in absolute cover units) under a clustered cyclone regime compared to stochastic or regular regimes. Thus, not only does cyclone clustering vary significantly along the GBR but such clustering is predicted to have a marked, and management-relevant, impact on the status of coral populations. Additionally, we use our regional clustering and rate results to sample from a library of over 7000 synthetic cyclone tracks for the GBR. This allowed us to provide robust reef-scale maps of annual cyclone frequency and cyclone impacts on Acropora. We conclude that assessments of coral reef vulnerability need to account for both spatial and temporal cyclone distributions.

     

Towards more predictable and consistent landscape metrics across spatial scales

Habitat change and fragmentation are considered key drivers of environmental change and biodiversity loss. To understand and mitigate the effects of such spatial disturbances on biological systems, it is critical to quantify changes in landscape pattern. However, the characterization of spatial patterns remains complicated in part because most widely used landscape metrics vary with the amount of usable habitat available in the landscape, and vary with the scale of the spatial data used to calculate them. In this study, we investigate the nature of the relationship between intrinsic characteristics of spatial pattern and extrinsic scale-dependent factors that affect the characterization of landscape patterns. To do so, we used techniques from modern multivariate statistics to disentangle widely used landscape metrics with respect to four landscape components: extent (E), resolution (R), percentage of suitable habitat cover (P), and spatial autocorrelation level (H). Our results highlight those metrics that are less sensitive to change in spatial scale and those that are less correlated. We found, however, significant and complex interactions between intrinsic and extrinsic characteristics of landscape patterns that will always complicate researcher's ability to isolate purely landscape pattern driven effects from the effects of changing spatial scale. As such, our study illustrates the need for a more systematic investigation of the relationship between intrinsic characteristics and extrinsic properties to accurately characterize observed landscape patterns.     

Detecting genomic clustering of risk variants from sequence data: cases versus controls

As the ability to measure dense genetic markers approaches the limit of the DNA sequence itself, taking advantage of possible clustering of genetic variants in, and around, a gene would benefit genetic association analyses, and likely provide biological insights. The greatest benefit might be realized when multiple rare variants cluster in a functional region. Several statistical tests have been developed, one of which is based on the popular Kulldorff scan statistic for spatial clustering of disease. We extended another popular spatial clustering method—Tango’s statistic—to genomic sequence data. An advantage of Tango’s method is that it is rapid to compute, and when single test statistic is computed, its distribution is well approximated by a scaled χ ² distribution, making computation of p values very rapid. We compared the Type-I error rates and power of several clustering statistics, as well as the omnibus sequence kernel association test. Although our version of Tango’s statistic, which we call “Kernel Distance” statistic, took approximately half the time to compute than the Kulldorff scan statistic, it had slightly less power than the scan statistic. Our results showed that the Ionita-Laza version of Kulldorff’s scan statistic had the greatest power over a range of clustering scenarios.     

A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data

Summary Identifying homogeneous groups of individuals is an important problem in population genetics. Recently, several methods have been proposed that exploit spatial information to improve clustering algorithms. In this article, we develop a Bayesian clustering algorithm based on the Dirichlet process prior that uses both genetic and spatial information to classify individuals into homogeneous clusters for further study. We study the performance of our method using a simulation study and use our model to cluster wolverines in Western Montana using microsatellite data.     

闽三角城市群生态环境脆弱性及其驱动力

生态安全是地区社会经济可持续发展的基本前提之一,而生态环境脆弱性会威胁地区的生态安全,进行生态环境脆弱性评价具有重要意义。以闽三角城市群为例,分别选取2000年和2015年的高程、坡度、岩性、土壤类型、NDVI、年均降水量、年均温、人口密度、人均GDP、路网密度、景观多样性、土地利用程度和工业固体废弃物排放量等13个指标,基于空间主成分分析法,并结合全局Moran'I和LISA聚类图,从整体特征、空间差异、空间集聚、地类脆弱性分异以及驱动力5个方面,探讨闽三角城市群的生态环境脆弱性及其驱动力。结果表明:2000—2015年间,闽三角城市群的生态环境脆弱性整体处于中度脆弱,但呈现出由中度脆弱向重度脆弱过渡的趋势;2000—2015年间,生态环境脆弱性呈现出由东南沿海向西北内陆逐渐增强的整体趋势,并存在明显的地带性特征,东南沿海增长不明显,部分地区有所下降,而西北内陆增长明显;生态环境脆弱性存在显著的空间自相关性,且为显著正相关,在西北部山区地带为显著的高高聚集,在东南沿海平原地带为显著的低低聚集,16年间集聚性特征在空间上有所迁移和扩张;2000—2015年间,生态环境脆弱性在各地类存在着一定的异质性,其生态环境脆弱性指数大小顺序始终为:林地未利用地草地耕地水域建设用地,整体由中度脆弱向重度脆弱过渡;2000—2015年间,生态环境脆弱性的驱动力有所变化,但人口密度、景观多样性、岩性和土壤类型一直是闽三角城市群生态环境脆弱性主要的驱动力。     

城市扩张背景下城市群环境问题的区域性特征分析

作为区域空间组织的重要形式,城市群已经成为促进城市化快速发展的重要地区,同时也逐渐成为生态环境问题高度集中的地区。采用夜晚灯光影像在分析京津冀、长江三角洲和珠江三角洲城市群空间扩张进程的基础上,从空气质量、工业固体废物处置利用情况以及植被覆盖度3个方面对三大城市群环境问题的区域性特征及其敏感区进行分析。结果表明:三大城市群空间扩张进程不断加快,并逐渐形成城市用地连绵区,长江三角洲城市群城市用地连绵区范围最大,珠江三角洲城市群城市用地连绵区范围高于京津冀城市群;从城市群层面看,三大城市群在空气质量、工业固体废物综合利用率以及植被覆盖度演变方面已呈区域性特征,城市化水平与工业固体废物综合利用率呈正相关,与城市群植被覆盖度呈负相关;从城市层面来看,空间扩张较快的宁波市和北京市为长江三角洲和京津冀城市群在空气质量、工业固体废物综合利用率以及植被覆盖度方面的敏感区,中山市为珠江三角洲在工业固体废物综合利用率以及植被覆盖度方面的敏感区。环境问题的区域特征以及环境敏感区分析对环境治理工作具有一定的指导作用,对区域的可持续发展具有一定的现实意义。     

SPATIAL GENETIC STRUCTURE OF CLONAL AND SEXUAL REPRODUCTION IN POPULATIONS OF ADENOPHORA GRANDIFLORA (CAMPANULACEAE)

The spatial distribution of clonal versus sexual reproduction in plant populations should generally have differing effects on the levels of biparental inbreeding and the apparent selfing rate, produced via mating by proximity through limited pollen dispersal. We used allozyme loci, join-count statistics, and Moran's spatial autocorrelation statistics to separate the spatial genetic structure caused by clonal reproduction from that maintained in sexually reproduced individuals in two populations of Adenophora grandiflora, a perennial herb. Join-count statistics showed that there were statistically significant clustering of clonal genotypes within distances less than 4 m. Both the entire populations and the sets of sexually reproduced individuals exhibited significant spatial autocorrelation at less than about 12 m, and the sexually reproduced individuals are substantially structured in an isolation-by-distance manner, consistent with a neighborhood size of about 50.     

Statistical models for spatial analysis in parasitology

The simplest way to study the spatial pattern of a disease is the geographical representation of its cases (or some indicators of them) over a map. Maps based on raw data are generally "wrong" since they do not take into consideration for sampling errors. Indeed, the observed differences between areas (or points in the map) are not directly interpretable, as they derive from the composition of true, structural differences and of the noise deriving from the sampling process. This problem is well known in human epidemiology, and several solutions have been proposed to filter the signal from the noise. These statistical methods are usually referred to as Disease Mapping. In geographical analysis a first goal is to evaluate the statistical significance of the heterogeneity between areas (or points). If the test indicates rejection of the hypothesis of homogeneity the following task is to study the spatial pattern of the disease. The spatial variability of risk is usually decomposed into two terms: a spatially structured (clustering) and a non spatially structured (heterogeneity) one. The heterogeneity term reflects spatial variability due to intrinsic characteristics of the sampling units (e.g. igienic conditions of farms), while the clustering term models the association due to proximity between sampling units, that usually depends on ecological conditions that vary over the study area and that affect in similar way breedings that are close to each other. Hierarchical bayesian models are the main tool to make inference over the clustering and heterogeneity components. The results are based on the marginal posterior distributions of the parameters of the model, that are approximated by Monte Carlo Markov Chain methods. Different models can be defined depending on the terms that are considered, namely a model with only the clustering term, a model with only the heterogeneity term and a model where both are included. Model selection criteria based on a compromise between degree of complexity and goodness of fit are then needed to discriminate among them, because each specification has a different biological meaning. Our aim is to demonstrate that these techniques can be used to study the geographical distribution of a parasite infection. Our analyses are based on data collected in 142 farms of the province of Latina. In each breeding a fixed number of sheeps has been sampled (20) and checked for the presence of C. daubneyi. We have specified a Binomial model for the proportion of infected animals in each breeding. The heterogeneity component is modelled in a standard way, while we have used different prior specifications for the clustering term to show how they affect the results. When we use the usual specification also for clustering, the two models show a completely different spatial pattern of infection, probably because the intrinsic spatial structure of the clustering term tend to bias our inferences. The selection criterion indicates in this case the heterogeneity model as the "best" one. However, if we modify the prior so that a lower degree of spatial interaction is assumed, the clustering model is less complex and its goodness of fit better and it should be preferred.     

Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

ABSTRACT: BACKGROUND: The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. RESULTS: We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest indexscore, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence-based methods. CONCLUSIONS: Appropriate homologous sequences are selected automatically and objectively by the index. Such sequence selection improved the performance of functional region prediction. As far as we know, this is the first approach in which spatial statistics have been applied t o protein analyses. Such integration of structure and sequence information would be useful for other bioinformatics problems.     

Representativity of a mesoscale network for weather-related factors governing pollen dispersal

The cultivation of transgenic crops, such as maize, requires successful gene isolation in field environments. Five spatial statistical techniques are used to evaluate the use of a regional mesoscale observation network (Iowa Environmental Mesonet) as a means to drive field-scale pollen dispersion modeling. The Nearest Neighbor Index, Fractal Dimension, Morisita Index, Thiessen Polygons, and Coefficient of Representativity are computed showing the positive and negative impacts of sequential addition of observation networks into a mesonet framework (a collection of pre-existing networks). While it is shown that the arbitrary combination of disparate observing networks increases spatial resolution, this improvement is often at the expense of increased clustering due to co-location of observation sites near urban areas. Network composition in terms of density and degree of clustering was evaluated with a grid analysis using the Barnes scheme as a means to mitigate clustering and improve prediction accuracies when mesonet data are applied to modeling. This paper shows the importance of understanding and accounting for the spatial characteristics of an observational network before applying it to a modeling effort such as field scale pollen dispersion.     

Spatial genetic pattern in the land mollusc Helix aspersa inferred from a 'centre-based clustering' procedure

The present work provides the first broad-scale screening of allozymes in the land snail Helix aspersa. By using overall information available on the distribution of genetic variation between 102 populations previously investigated, we expect to strengthen our knowledge on the spread of the invasive aspersa subspecies in the Western Mediterranean. We propose a new approach based on a centre-based clustering procedure to cluster populations into groups following rules of geographical proximity and genetic similarity. Assuming a stepping-stone model of diffusion, we apply a partitioning algorithm which clusters only populations that are geographically contiguous. The algorithm used, which is actually part of leading methods developed for analysing large microarray datasets, is that of the k-means. Its goal is to minimize the within-group variance. The spatial constraint is provided by a list of connections between localities deduced from a Delaunay network. After testing each optimal group for the presence of spatial arrangement in the genetic data, the inferred genetic structure was compared with partitions obtained from other methods published for defining homogeneous groups (i.e. the Monmonier and SAMOVA algorithms). Competing biogeographical scenarios inferred from the k-means procedure were then compared and discussed to shed more light on colonization routes taken by the species.     

基于结构的单绕蛋白聚类图构建与分析

蛋白质分子进化规律研究是分子进化研究的重点,对揭示生命起源与进化机制有重要意义。本文对已知空间结构及物种信息的单绕蛋白,利用结构比对信息,构建了不同层次单绕样本系统聚类图。分析发现:功能相似蛋白存在明显聚集现象,同一超家族样本基本聚在一个大支中,同一家族样本集中在所属超家族下的小支中,功能约束下单绕样本聚类图与物种进化图有较好对应关系。结果表明:单绕蛋白的结构演化反映了蛋白质功能的约束,特定功能单绕样本的结构差异具有种属特异性,结构演化包含了物种进化信息。     

Assessing the influence of environmental heterogeneity on bird spacing patterns: a case study with two raptors

Testing for aggregation or regularity in point patterns is difficult in the presence of spatial variation in abundance due to environmental heterogeneity. Using a recently developed method generalizing Ripley's K function for non homogeneous point patterns, we test the aggregation of the nests in two species of birds (little owl and Montagu's harrier) exhibiting heterogeneous distributions in response to landscape structure. We compare the results obtained under different null models accounting for environmental heterogeneity at large and/or small spatial scales.
Whereas both species were initially found to form clusters at some scale, taking spatial heterogeneity into account revealed that 1) territorial little owls showed no clustering of territories when habitat availability was considered; 2) semi-colonial harriers still formed significant clusters, but part of the aggregation in this species could be explained by landscape structure alone. Our results highlight that it is feasible and highly recommended to account for non-stationarity when testing for aggregation. Further, provided that sufficient knowledge of the study system is available, this approach helps to identify behavioural and environmental components of spatial variation in abundance. Additionally, we demonstrate that accounting for large or small-scale heterogeneity affects the perception of spacing behaviours differently, so that both need to be considered.