Using Weight Functions in Spatial Point Pattern Analysis with Application to Plant Ecology Data

In the analysis of spatial point patterns, complete spatial randomness (CSR) hypothesis,which is a restriction of a homogenous Poisson process to study region A, operates as a dividinghypothesis between “regular” and “aggregated” patterns. Meanwhile, many alternatives to CSR inaggregated patterns are extensions of homogenous Poisson processes themselves. Therefore, when theCSR hypothesis is rejected, results related to Poisson processes may be used to formulate plausiblealternatives to CSR. In this paper, we propose a new statistic for testing CSR and then by applying it inconjunction with a notion of kernels of a point pattern, we determine the “parents” of a Poisson clusterprocess when the CSR hypothesis is rejected and a Neyman-Scott process is assumed for the pointpattern under alternative hypothesis. We have made power studies for our test statistic by simulation, andhave also surveyed the performance of our method on a certain point pattern. Finally, the whole methodis carried on certain real life data.

PDF HTML阅读 XML下载 导出引用 引用提醒 生态学中的点格局研究概况及其在国内的应用 DOI: 10.5846/stxb201607081399 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然基金项目（31370633）。 A review of point pattern analysis in ecology and its application in China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:点格局分析是研究生态学格局的工具之一，近年来在生态学中的应用越来越多。为深入了解点格局分析方法在国内的研究与应用情况，以所总结的研究进展、一般步骤和基本要点为背景，分析评述了1996-2015年期间以点格局为主题的国内中文核心期刊文献。结果表明，在国内生态学格局研究中，应用研究占据主导地位，研究对象广泛，包括以树木为主的乔、灌、草等不同生活型的植物，甚至包括景观；基础研究，包括概括性统计量、零模型与点过程模型等方面，以及专用软件工具包的开发等研究薄弱。在应用中存在一定问题，主要表现为：概括性统计量使用单一，且以Ripley的K-函数及其变形为主；零模型（或点过程模型）是科学问题的统计表达，但是有一半以上的研究未明确给出零模型。建议在未来应用研究中重视多种统计量的组合使用和原假设的建立，在探讨热带、亚热带森林等具有复杂空间结构系统的多样性格局时，考虑对象的不同世代和系统的不同垂直层次，并加强多变量或三维概括性统计量的开发、点格局分析方法与动态过程模型的结合研究等工作。 Abstract:Point pattern analysis, i.e., spatial point pattern analysis (SPPA) is a tool used in ecological pattern research and has recently become increasingly popular in ecology. To better understand SPPA research and its application in China, in this review we examined articles regarding SPPA published from 1996 to 2015 in China, which was based on a summary of the research progress, using steps and key points of SPPA. It was found that most articles focused on application research. Although there were extensive study objects, including forests, shrubs, grasslands, and landscapes, trees in forest communities were the main focus in all these application studies. Fundamental studies were found to be underdeveloped, such as studies regarding summary statistics and null models or point process models in SPPA, and the development of specific software packages for SPPA. Two important problems were found in SPPA applications: Firstly, although there are many alternatives, researchers preferred single summary statistics and only selected the K-function or its transformations; and secondly, although selecting null models as null hypotheses was a key step in SPPA, more than half of the research examined did not specify explicit null models. Therefore, it is recommended that different types of summary statistics are used to capture detailed spatial pattern structures, and the ecological questions or hypotheses should be clearly stated and correctly translated into statistical language, i.e., the null model and tests. Vertical layers and different generations should also be considered when studying systems with complex spatial structures, such as in tropical rainforests and subtropical evergreen broad-leaved forests. To enhance basic SPPA applications, these aspects discussed should be strengthened. 参考文献 相似文献 引证文献

To study the quantified indices for describing the distributional status of diseases in the spatial point pattern analysis, and provide the a statistic in disease prevention and control. G function, F function, J function and K function were summarized based on the inter-case distances from the view of spatial point pattern analysis. Through the introduction of the basic principles, these were used to analyze the data of acute schistosomiasis in the Guichi District, Chizhou City, Anhui province, with the study distances being from 0 to 3000 meters with 50-meter intervals. The findings were also validated by means of spatial moving scan window performed in SaTScan software. A total of 83 cases of acute schistosomiasis identified in Guichi District, and the point map showed that these cases were mainly distributed around the Yangtze and Qiupu rivers. The computational methods and characteristics of the four quantified indices were obtained. These acute schistosomiasis cases were also explored by using these indices, and the results showed that C and K functions were above 95% confidence interval. While, F and J functions were below 95% confidence interval. Ml these four indices showed that spatial clustering existed in the acute cases, which was consistent with the results of spatial moving scan window method. The latter method also found a most likely cluster, the coordinate of the circle center is (30.65 N, 117.44 E), radius is 2.69 km, and relative risk is 12.78 (DIR = 32. 80, P = 0. 0001). The quantified indices to describe the distributional status of diseases have not only solved the obstacle that spatial point pattern map which could only be analyzed qualitatively, but also supplied a theoretical foundation to deepen spatial clustering analysis. To study the quantified indices for describing the distributional status of

Single tree detection is important for monitoring tree plantations in order to estimate crop yield, which can be used for assessing food and biofuels. In the field of remote sensing, there have been studies regarding single tree extraction using Light Detection and Ranging (LiDAR) point cloud data. Canopy Height Model (CHM) can be simply derived from these LiDAR data where trees can be analysed using different physical characteristics such as tree crown width, and height. However, using these physical characteristics can’t be very useful for identifying which trees are within a plantation, a forest, or a single tree. This is the motivation for developing algorithms of automated differentiation of random trees from plantation trees. Object Based Image Analysis (OBIA) was used to acquire the locations of individual trees. Trees were separated from other land cover in the image using a CHM derived from LiDAR point cloud data. Using watershed algorithm in eCognition, trees with good canopy spacing was easily distinguished. A spatial point pattern analysis was done from the extracted locations of trees to differentiate regularly patterned points from randomly distributed points. Nearest neighbour statistics was done to provide measurement of the distribution of the extracted points. The Z-Score parameter from nearest neighbor was then added to the layers in eCognition as supplement to the available height derivatives such as mean, standard deviation, and different textural parameters. Supervised type of classification was used in order to classify the trees on the image. Training samples of each tree from the image were entered in Support Vector Machine (SVM) to classify the different trees. Two sets of validation were done to compare the results of using the object parameters only, and the object parameters and Z-Score. Addition of the Z-Score from the object parameters increased the obtained accuracy on both validation sets. Classification accuracy based on validation set 1 and set 2 increased by about 20% and 6%, respectively. Additional spatial pattern analysis methods and validation can further prove the use of such layers in improving classification of trees.

This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments.

There is considerable interest in cell biology in determining whether, and to what extent, the spatial arrangement of nuclear objects affects nuclear function. A common approach to address this issue involves analyzing a collection of images produced using some form of fluorescence microscopy. We assume that these images have been successfully pre-processed and a spatial point pattern representation of the objects of interest within the nuclear boundary is available. Typically in these scenarios, the number of objects per nucleus is low, which has consequences on the ability of standard analysis procedures to demonstrate the existence of spatial preference in the pattern. There are broadly two common approaches to look for structure in these spatial point patterns. First a spatial point pattern for each image is analyzed individually, or second a simple normalization is performed and the patterns are aggregated. In this paper we demonstrate using synthetic spatial point patterns drawn from predefined point processes how difficult it is to distinguish a pattern from complete spatial randomness using these techniques and hence how easy it is to miss interesting spatial preferences in the arrangement of nuclear objects. The impact of this problem is also illustrated on data related to the configuration of PML nuclear bodies in mammalian fibroblast cells.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community‐level patterns, and ecological processes. In this study, we developed a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub‐blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0–0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a geographical information system, to collect experimental data on the spatial point patterns for the populations in this grassland community.

This paper attempts to further the research by Odland and Ellis (1992) in applying event history methodology to the analysis of spatial point patterns (that is, event patterns). Its empirical focus is the event pattern derived from the adoption of an agricultural innovation, the Harvestore, in southern Ontario, Canada, from 1963 to 1986. Event history analysis involves the use of discrete‐state, continuous‐time stochastic models to investigate a temporal longitudinal record on discrete variables. Event history models are usually concerned with durations of time between events and the effects of intertemporal time dependencies on future event occurrences. As such, they are often referred to as duration models. Many of the methods used in event history analysis allow the use of other nonnegative interval measurements in place of standard temporal intervals to investigate a series of events. In particular, spatial intervals (or durations) of distances between events may also be accommodated by event history models. Our analysis extends the previous research of Odland and Ellis (1992) by using a wider range of parametric models to explore duration dependence, investigating the role of spatial censoring, and using a more extensive set of explanatory variables. In addition, simulation experiments and graphical tests are used to evaluate the empirical event pattern against one generated from Complete Spatial Randomness. Results indicate that the event pattern formed by the Harvestore adopter farms is clustered (that is, is described by positive duration dependency), the sales agent is a significant factor in the distribution of adopters, and that contrasting results are obtained from the analysis using censored data versus uncensored data.

Spatial point pattern analysis applied to bubble nucleation in silicate melts

The divergent gap characteristics and spatial patterns of canopy gaps created by natural or artificial disturbances can exert a dominant influence on forest structure and composition. However, little research has been conducted on the effects of ice storm damage on canopy gaps in subtropical mature forests of South China. In this study, one semi-natural site was dominated by a broad-leaf forest and two managed sites were representative of plantations with coniferous forests. Based on airborne laser scanning data and field evidence across sites, statistical analyses were used to examine gap characteristics following ice storms of moderate severity. Generalized Ripley’s K-function analysis was applied to test gap spatial patterns at a range of scales, and spatial point pattern analysis was used to quantitate the relative importance of specific influences on patterns of gap occurrence. The results revealed that the average gap size was 75.7 m2 and that 12.2 gaps occurred per hectare. Most gaps were single-tree fall events. In addition to more gaps, the mean gap size was smaller and the shape was more complex in the semi-natural site than in two managed sites. Large differences in gap characteristics were observed among snapped, uprooted, snag, and artificial gap damage types. Gaps generally showed a clustered distribution at large scales (e.g. 70m), whereas spatial patterns varied with gap damage types at different sites. The occurrence of gaps was strongly related to slope and topographic position at the semi-natural site, whereas slope, stem density, and human accessibility (proximity to pathways) were the most important factors affecting gap occurrence at the managed sites. We suggest that gap-based silvicultural treatments and natural disturbances regimes conjoin, highlighting interactions with other factors such as microsite conditions, non-tree vegetation and more.

IntroductionPopulation-level competition and spatial patterns may explain the role of competitive exclusion in communities, which is important for vegetation restoration and biodiversity conservation.MethodsWe analyzed the competitive intensity, spatial patterns, and renewal of Populus euphratica Oliv. forests in the Tarim River Basin using the Hegyi competition index and spatial point pattern analysis in a completely random model with different habitats and different forest ages. ResultsThe greatest competitive distance for P. euphratica was 10 m, and the intensity of competition steadily decreased as the diameter increased. The intensity of intraspecific and interspecific competition in young, mature, and old P. euphratica forests was as follows: riverside habitat > transitional habitat > desert margin habitat. The Simpson index values for the three habitats decreased as follows: transitional > riverside > desert margin, and the Shannon-Wiener index and Pielou index values decreased as follows: riverside > transitional > desert margin. In the riverside habitat, the young P. euphratica forest experienced the greatest competitive intensity, the mature forest in the transitional habitat was the largest, and the forest in the desert margin habitat was the oldest. Competitive intensity was greatest in the young riverside P. euphratica forest, mature P. euphratica forest in the transitional habitat, and old forest in the desert margin. Riverside P. euphratica experienced strong competition from Populus pruinosa. Competitive exclusion caused P. pruinosa to disappear from the transitional and desert margin habitats. Young, mature, and old P. euphratica forests were randomly distributed along the riverside and in the transitional habitat, while mature and old P. euphratica forests were randomly distributed in the desert margin. Populus pruinosa, Tamarix ramosissima, and Tamarix hispida were mainly randomly distributed, and T. ramosissima and T. hispida were clustered at small scales. In the riverside habitat, young, mature, and old P. euphratica had no spatial correlation, and there was a significant negative correlation at small scales in the transitional habitat. The density of P. euphratica seedlings in the riverside habitat was greater than that in the transitional habitat, and greater competitive pressures on P. euphratica tree seedlings caused a lower renewal density.ConclusionsWhen planting P. euphratica forests, spacing greater than 10 m can effectively reduce stand competition and thus promote seedling regeneration.

A GIS tool for plant spatial pattern analysis

Forest community structure may be influenced by seedling density dependence, however, the effect is loosely coupled with population dynamics and diversity in the short term. In the long term the strength of conspecific density dependence may fluctuate over time because of seedling abundance, yet few long‐term studies exist. Based on 11 years of seedling census data and tree census data from a 25‐ha temperate forest plot in Northeast China, we used generalized linear mixed models to test the relative effects of local neighborhood density and abiotic factors on seedling density and seedling survival. Spatial point pattern analysis was used to determine if spatial patterns of saplings and juveniles, in relation to conspecific adults, were in accordance with patterns uncovered by conspecific negative density dependence at the seedling stage. Our long‐term results showed that seedling density was mainly positively affected by conspecific density, suggesting dispersal limitation of seedling development. The probability of seedling survival significantly decreased over 1 year with increasing conspecific density, indicating conspecific negative density dependence in seedling establishment. Although there was variation in conspecific negative density dependence at the seedling stage among species and across years, a dispersed pattern of conspecific saplings relative to conspecific adults at the local scale (<10 m) was observed in four of the 11 species examined. Overall, sapling spatial patterns were consistent with the impacts of conspecific density on seedling dynamics, which suggests that conspecific negative density dependence is persistent over the long term. From the long‐term perspective, conspecific density dependence is an important driver of species coexistence in temperate forests.

The importance of conspecific facilitation during recruitment and regeneration: A case study in degraded mangroves

Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matern covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.