Nonparametric One‐way Analysis of Variance of Replicated Bivariate Spatial Point Patterns

A common problem in neuropathological studies is to assess the spatial patterning of cells on tissue sections and to compare spatial patterning between disorder groups. For a single cell type, the cell positions constitute a univariate point process and interest focuses on the degree of spatial aggregation. For two different cell types, the cell positions constitute a bivariate point process and the degree of spatial interaction between the cell types is of interest. We discuss the problem of analysing univariate and bivariate spatial point patterns in the one‐way design where cell patterns have been obtained for groups of subjects. A bootstrapping procedure to perform a nonparametric one‐way analysis of variance of the spatial aggregation of a univariate point process has been suggested by Diggle, Lange and Bene? (1991). We extend their replication‐based approach to allow the comparison of the spatial interaction of two cell types between groups, to include planned comparisons (contrasts) and to assess whole groups against complete spatial randomness and spatial independence. We also accommodate several replicate tissue sections per subject. An advantage of our approach is that it can be applied when processes are not stationary, a common problem in brain tissue sections since neurons are arranged in cortical layers. We illustrate our methods by applying them to a neuropathological study to investigate abnormalities in the functional relationship between neurons and astrocytes in HIV associated dementia. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Computer Programme Package for the Statistical Analysis of Spatial Point Processes in a Square

A computer programme for the statistical analysis of point data in a square is described. Several tests for randomness of the distribution of points are possible. The most comprehensive of these are comparisons of the empirical distributions of the inter-point and closest neighbour distances with their respective expected distributions under complete randomness, and tests based on Ripley' L function; using these, significant aggregation or regularity can be identified. It is also possible to calculate statistics of properties (“attributes”) associated with each spatial point, as well as to compare statistics for sub-areas of the experimental square. Several measures of spatial autocorrelation are available, amongst them correlograms and variograms. The programme can also find the tesselation of the study area and correlate tile properties with the point attributes. The procedures are illustrated by references to the spatial distribution and mound heights of Trinevitermes trinervoides on a study area in South Africa. Although the programme was developed specifically for application in entomology, it could be used to analyse data from many other disciplines.     

Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles

A mixed model is a flexible tool for joint modeling purposes, especially when the gathered data are unbalanced. However, computational problems due to the dimension of the joint covariance matrix of the random effects arise as soon as the number of outcomes and/or the number of used random effects per outcome increases. We propose a pairwise approach in which all possible bivariate models are fitted, and where inference follows from pseudo-likelihood arguments. The approach is applicable for linear, generalized linear, and nonlinear mixed models, or for combinations of these. The methodology will be illustrated for linear mixed models in the analysis of 22-dimensional, highly unbalanced, longitudinal profiles of hearing thresholds.     

Parametric inference of neuronal response latency in presence of a background signal

Neurons are commonly characterized by spontaneous generation of action potentials (spikes), which appear without any apparent or controlled stimulation. When a stimulus is applied, the spontaneous firing may prevail and hamper identification of the effect of the stimulus. Therefore, for any rigorous analysis of evoked neuronal activity, the presence of spontaneous firing has to be taken into account. If the background signal is ignored, however small it is compared to the response activity, and however large is the delay, estimation of the response latency will be wrong, and the error will persist even when sample size is increasing. The first question is: what is the response latency to the stimulus? Answering this question becomes even more difficult if the latency is of a complex nature, for example composed of a physically implied deterministic part and a stochastic part. This scenario is considered here, where the response time is a sum of two components; the delay and the relative latency. Parametric estimators for the time delay and the response latency are derived. These estimators are evaluated on simulated data and their properties are discussed. Finally, we show that the mean of the response latency is always satisfactorily estimated, even assuming a wrong distribution for the response latency.     

On Second Order Neighbourhood Analysis of Mapped Point Patterns

Getis and Franklin (1987), introduced a technique based on second order methods, called second order neighbourhood method, which is used to quantify clustering at various spatial scales. Variants of this method are introduced for testing whether a spatial point pattern is consistent with the hypothesis of a Poisson process. These variants are applied to point location data for a sample of Ponderosa pine (Pinus ponderosa) trees.     

早稻田蜘蛛和目标害虫空间分布的动态分析

通过采用聚集度指标的方法,在早稻不同发育期,对稻田蜘蛛及目标害虫飞虱和叶蝉的空间分布进行了系统研究,测定结果表明其分布型随水稻发育期的变更而变化,为害虫综合防治策略的制定提供依据。     

Hierarchical Modeling for Estimating Relative Risks of Rare Genetic Variants: Properties of the Pseudo‐Likelihood Method

Summary Many major genes have been identified that strongly influence the risk of cancer. However, there are typically many different mutations that can occur in the gene, each of which may or may not confer increased risk. It is critical to identify which specific mutations are harmful, and which ones are harmless, so that individuals who learn from genetic testing that they have a mutation can be appropriately counseled. This is a challenging task, since new mutations are continually being identified, and there is typically relatively little evidence available about each individual mutation. In an earlier article, we employed hierarchical modeling ( Capanu et al., 2008 , Statistics in Medicine 27 , 1973–1992) using the pseudo‐likelihood and Gibbs sampling methods to estimate the relative risks of individual rare variants using data from a case–control study and showed that one can draw strength from the aggregating power of hierarchical models to distinguish the variants that contribute to cancer risk. However, further research is needed to validate the application of asymptotic methods to such sparse data. In this article, we use simulations to study in detail the properties of the pseudo‐likelihood method for this purpose. We also explore two alternative approaches: pseudo‐likelihood with correction for the variance component estimate as proposed by Lin and Breslow (1996, Journal of the American Statistical Association 91 , 1007–1016) and a hybrid pseudo‐likelihood approach with Bayesian estimation of the variance component. We investigate the validity of these hierarchical modeling techniques by looking at the bias and coverage properties of the estimators as well as at the efficiency of the hierarchical modeling estimates relative to that of the maximum likelihood estimates. The results indicate that the estimates of the relative risks of very sparse variants have small bias, and that the estimated 95% confidence intervals are typically anti‐conservative, though the actual coverage rates are generally above 90%. The widths of the confidence intervals narrow as the residual variance in the second‐stage model is reduced. The results also show that the hierarchical modeling estimates have shorter confidence intervals relative to estimates obtained from conventional logistic regression, and that these relative improvements increase as the variants become more rare.     

湖南壶瓶山国家级自然保护区两栖 爬行动物多样性的时空格局

本研究利用2013至2016年湖南壶瓶山国家级自然保护区巡护监测数据,评估壶瓶山保护区两栖爬行动物多样性水平。壶瓶山保护区在8个监测站点布设了25条两栖爬行动物调查样线,进行常规监测,其中,日间样线14条,夜间样线11条,日间样线平均长度4 km,夜间样线平均长度0.75 km。每个月,每条样线平均调查3次。4年共调查到62种两栖爬行动物,占湖南省两栖爬行动物总种数的38.04%。逐月计算各监测站点的alpha多样性指数和整个保护区的beta多样性指数,按季节比较不同站点间物种组成的Hellinger距离。结果显示,保护区不同位置间、不同的监测站点间以及年际间的两栖爬行动物多样性没有显著差异,提示保护区环境具有相对稳定性。但是,个别位于实验区或邻近实验区的站点,物种组成有着剧烈的波动,表明人为活动影响了两栖爬行动物的分布。另外,保护区两栖爬行动物的物种组成与多样性有着显著的季节差异,这与两栖爬行动物的生物学特性有关。以上结果说明,壶瓶山保护区两栖爬行动物的多样性结构具有稳定性和敏感性的特点,可以考虑用作保护区生物多样性水平的监测指标。     

Saddlepoint Approximations to the Moments of Multitype Age‐Dependent Branching Processes,with Applications

Summary This article proposes saddlepoint approximations to the expectation and variance–covariance function of multitype age‐dependent branching processes. The proposed approximations are found accurate, easy to implement, and much faster to compute than by simulating the process. Multiple applications are presented, including the analyses of clonal data on the generation of oligodendrocytes from their immediate progenitor cells, and on the proliferation of Hela cells. New estimators are also constructed to analyze clonal data. The proposed methods are finally used to approximate the distribution of the generation, which has recently found several applications in cell biology.     

Model-Based Statistical Procedures for the Analysis of in Vitro Mutagenesis Assays

A number of short-term screening assays for potential chemical carcinogens and mutagens involve the measurement of mutation rates in in vitro cell populations. In this paper, statistical procedures are proposed for the analysis of results from these assays. The procedures, which are based on previously developed stochastic models of cell growth and mutation, include hypothesis tests for the comparison of mutation rates in treated and control cultures, and estimation and hypothesis tests for the parameters of a linear mutagenesis dose-response. Computer simulation is used to validate the methods, and they are illustrated by an analysis of data obtained under the mouse lymphoma mutagenesis protocol.     

A Modeling Study on Responses of Alkaline Grassland Ecosystems to Climate Change in Light of Diversity and Spatial Patterns

Temporal variations of spatial coverage patterns of major plant species in the alkaline grasslands in northeast China subject to climate change were studied using a spatial simulation model. The model stressed the coupling between soil alkali and vegetation coverage. The modeled species coverage patterns were shown in close agreement with observations on a fenced one-hectare alkaline grassland from 1989 to 1993. The impacts of climate change on the species coverage were studied by subtracting the output patterns of the model under contemporary climate from those under altered climate. To relate the difference patterns to various landscape indices, the one-hectare region was divided into 25 subregions. The differencein species coverage between the present and altered climate and spatial pattern and diversity indices were computed for each subregion. A statistical analysis showed that for plants with wide ranges of tolerance to soil alkali and strong spatial migration capability, the impact of climate change was significantly related to spatial patterns, but not to diversity. However, for plants with narrow ranges of tolerance to soil alkali and less capability to migrate spatially, the impact of climate change was related to both diversity and spatial patterns.     

Modeling Group Formation and Activity Patterns in Self-Organizing Collectives of Individuals

We construct and analyze a nonlocal continuum model for group formation with application to self-organizing collectives of animals in homogeneous environments. The model consists of a hyperbolic system of conservation laws, describing individual movement as a correlated random walk. The turning rates depend on three types of social forces: attraction toward other organisms, repulsion from them, and a tendency to align with neighbors. Linear analysis is used to study the role of the social interaction forces and their ranges in group formation. We demonstrate that the model can generate a wide range of patterns, including stationary pulses, traveling pulses, traveling trains, and a new type of solution that we call zigzag pulses. Moreover, numerical simulations suggest that all three social forces are required to account for the complex patterns observed in biological systems. We then use the model to study the transitions between daily animal activities that can be described by these different patterns.     

宁夏蝗虫地理分布格局的聚类分析

根据宁夏的自然环境和行政区划将宁夏分为22个分布区域。为方便聚类分析,根据分布范围将宁夏蝗虫分为全布种(22个区域有分布)、多布种(2～21区域有分布)和独布种(只分布在1个区域)3类。在以分布区域为特征做聚类分析的基础上,把多布种分组并确定重叠的分布格局,共分为14个组。各组蝗虫在宁夏的自然分布作为其分布格局。结果表明：宁夏蝗虫的多布种多呈不连续分布;分布格局与国内外基本相似;贺兰山是宁夏蝗虫分布的热点。不连续的环境是造成宁夏蝗虫多布种不连续分布的主要原因。     

基于红外相机技术分析鼬獾的活动节律

鼬獾(Melogale moschata)是食肉目鼬科鼬獾属动物,在我国分布广泛、种群数量丰富,但有关鼬獾的生态研究报导比较少.为掌握鼬獾的活动节律及其影响因素,2017年2月至2019年2月,利用红外相机技术对江西省桃红岭梅花鹿国家级自然保护区、九岭山国家级自然保护区和齐云山国家级自然保护区的鼬獾进行了监测,每个保护...     

云南地区种子植物多样性分布格局和多样性分化特点研究

利用大尺度的植物分布信息结合地理信息系统和数理统计分析,探讨了云南种子植物多样性的地理分布格局和多样性分化特点。结果表明,从科到属和种,植物多样性和区系分化强度的数据集离散程度均呈递增趋势。相对属和种而言,科的多样性的空间分异和高值区域并不明显,这可能与科、属、种所代表的不同地质历史时期有关;南部地区属和种的多样性明显高于除滇西北以外的大部分地区。种的多样性分布中心主要集中在滇西北地区、云南南部地区、哀牢山、无量山地区和昆明地区,这可能与生境异质性和热量条件有关。相对科而言,属的区系分化强度的空间分异更为明显,并与物种多样性的分布格局比较接近,暗示着植物多样性与区系分化之间的密切联系。相对科而言,属的区系分化中心高值区域更为明显。总体上,云南地区植物多样性的分布格局及其分化可能与其生境、热量条件和地质历史有关。     

A Survey of Sample Size Formulas for Pairwise and Many‐one Multiple Comparisons in the Parametric,Nonparametric and Binomial Case

We present a survey of sample size formulas derived in other papers for pairwise comparisons of k treatments and for comparisons of k treatments with a control. We consider the calculation of sample sizes with preassigned per‐pair, any‐pair and all‐pairs power for tests that control either the comparisonwise or the experimentwise type I error rate. A comparison exhibits interesting similarities between the parametric, nonparametric and binomial case.     

Patterns in vegetation and sedimentation during the Weichselian Late-glacial in north-eastern Germany

Aim To identify and interpret spatial patterns of vegetation and sedimentation during the Weichselian Late‐glacial. Location North‐eastern Germany and the adjacent fringe of north‐western Poland. Methods An inspection and comparison of palynological data from c. 150 sites. Results Open Vegetation phase I (Oldest Dryas = earlier part of the Meiendorf, 12,900–12,450 ¹⁴C bp ) and the Hippophaë phase (Bølling = later part of the Meiendorf; 12,450–12,000 bp ) were rather homogeneous palynologically in the study area. Open Vegetation phase II (Older Dryas; 12,000–11,900 bp ) is strongly recorded in the northern part of the study area with relatively thick sediments (suggesting severe soil erosion), but it can hardly be traced 200 km further to the south. This is attributed to sea buckthorn (Hippophaë) shrubs persisting longer in the south due to higher temperatures, to Betula forests expanding earlier under the influence of a more humid climate or to a generally denser vegetation independent of the behaviour of Hippophaë and Betula. During the late‐glacial Betula/Pinus forest phase (Allerød; 11,900–11,000 bp ), pine (Pinus) forests dominated in the southern regions, whereas birch (Betula) forests prevailed in the north. Open Vegetation phase III (Younger Dryas; 11,000–10,000 bp ) was characterized by heathlands in the northern regions with scattered birches and with sedimentation dominated by in‐washed silicates. In the south, pine parklands occurred with sedimentation dominated by local primary production which had markedly decreased after the previous warmer vegetation phase. Main conclusions The differences in vegetation and sedimentation during the open vegetation phases are attributed to a colder climate in the north than in the south, probably related to a climatic gradient between the ice‐free continental central Europe and the decaying Scandinavian ice sheet. The vegetation patterns during the late‐glacial Betula/Pinus forest phase are attributed to edaphic differences between the predominantly till plains in the northern part of the study area and the prevailing sandy outwash plains and Urstromtäler of the southern regions.