A Non-Gaussian Spatial Generalized Linear Latent Variable Model

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents.     

Finite Mixture of Regression Modeling for High-Dimensional Count and Biomass Data in Ecology

Understanding how species distributions respond as a function of environmental gradients is a key question in ecology, and will benefit from a multi-species approach. Multi-species data are often high dimensional, in that the number of species sampled is often large relative to the number of sites, and are commonly quantified as either presence–absence, counts of individuals, or biomass of each species. In this paper, we propose a novel approach to the analysis of multi-species data when the goal is to understand how each species responds to their environment. We use a finite mixture of regression models, grouping species into “Archetypes” according to their environmental response, thereby significantly reducing the dimension of the regression model. Previous research introduced such Species Archetype Models (SAMs), but only for binary assemblage data. Here, we extend this basic framework with three key innovations: (1) the method is expanded to handle count and biomass data, (2) we propose grouping on the slope coefficients only, whilst the intercept terms and nuisance parameters remain species-specific, and (3) we develop model diagnostic tools for SAMs. By grouping on environmental responses only, the model allows for inter-species variation in terms of overall prevalence and abundance. The application of our expanded SAM framework data is illustrated on marine survey data and through simulation. Supplementary materials accompanying this paper appear on-line.     

Multilevel Latent Gaussian Process Model for Mixed Discrete and Continuous Multivariate Response Data

We propose a Bayesian model for mixed ordinal and continuous multivariate data to evaluate a latent spatial Gaussian process. Our proposed model can be used in many contexts where mixed continuous and discrete multivariate responses are observed in an effort to quantify an unobservable continuous measurement. In our example, the latent, or unobservable measurement is wetland condition. While predicted values of the latent wetland condition variable produced by the model at each location do not hold any intrinsic value, the relative magnitudes of the wetland condition values are of interest. In addition, by including point-referenced covariates in the model, we are able to make predictions at new locations for both the latent random variable and the multivariate response. Lastly, the model produces ranks of the multivariate responses in relation to the unobserved latent random field. This is an important result as it allows us to determine which response variables are most closely correlated with the latent variable. Our approach offers an alternative to traditional indices based on best professional judgment that are frequently used in ecology. We apply our model to assess wetland condition in the North Platte and Rio Grande River Basins in Colorado. The model facilitates a comparison of wetland condition at multiple locations and ranks the importance of in-field measurements.     

Modelling the Covariance Structure in Marginal Multivariate Count Models: Hunting in Bioko Island

The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson–Tweedie distribution. Furthermore, the score information criterion is extended for selecting the components of the matrix linear predictor. We analyse a data set consisting of prey animals (the main hunted species, the blue duiker Philantomba monticola and other taxa) shot or snared for bushmeat by 52 commercial hunters over a 33-month period in Pico Basilé, Bioko Island, Equatorial Guinea. By taking into account the severely unbalanced repeated measures and longitudinal structures induced by the hunters and a set of potential covariates (which in turn affect the mean and covariance structures), our method can be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited areas anywhere in the world. We suggest that our method can be used to more accurately understand the trajectories of animals hunted for commercial or subsistence purposes and establish clear policies to ensure sustainable hunting practices.Supplementary materials accompanying this paper appear online.     

Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Models

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.     

陕北黄土丘陵山地枣树生物量模型

[目的]探索一种在不损伤枣树条件下估算枣树生物量的方法,为研究灌溉施肥与枣树生长关系提供技术支撑。[方法]在大量实际测定调查数据的基础上,用线性和非线性生物量模型分别对枣树枝条、枣吊、叶片及果实的生物量进行建模,以相关系数R2,拟合指数W,标准误SEE,变异系数CV和预估精度p作为模型的精度评价指标,对模型进行了检验,并对两类模型进行了对比分析。[结果]线性模型适用于枝条生物量的预测,R2为0.980~0.984,p介于0.909~0.926;叶片生物量和枣吊生物量的最优模型为非线性模型,R2介于0.880~0.943,p介于0.897~0.976;果实生物量最优模型为二元非线性模型,R2为0.999,p为0.998;枝条、叶片和枣吊的鲜重与干重的比例系数分别为2.085,2.854和2.675;单个枣吊长度和枣吊上的叶片数目呈现出良好的一元线性关系,R2达到0.88。[结论]本研究中使用的枣树生物量模型的模拟精度较高。     

Comparison of Models for Olfactometer Data

Olfactometer experiments are used to study the responses of arthropods to potential attractants, for purposes such as understanding natural defenses of plants against their herbivores. Such experiments typically lead to multivariate data consisting of small correlated counts, which are overdispersed relative to standard models. In this paper models that account for the overdispersion under different hypotheses on insect behavior are described and illustrated with an example, and a graphical approach to discriminating among them is briefly discussed.     

Co-clustering Spatial Data Using a Generalized Linear Mixed Model With Application to the Integrated Pest Management

Co-clustering has been broadly applied to many domains such as bioinformatics and text mining. However, model-based spatial co-clustering has not been studied. In this paper, we develop a co-clustering method using a generalized linear mixed model for spatial data. To avoid the high computational demands associated with global optimization, we propose a heuristic optimization algorithm to search for a near optimal co-clustering. For an application pertinent to Integrated Pest Management, we combine the spatial co-clustering technique with a statistical inference method to make assessment of pest densities more accurate. We demonstrate the utility and power of our proposed pest assessment procedure through simulation studies and apply the procedure to studies of the persea mite (Oligonychus perseae), a pest of avocado trees, and the citricola scale (Coccus pseudomagnoliarum), a pest of citrus trees.     

基于花粉量的作物产量预测模型研究进展

为了探究以花粉量为主要因子的作物产量预测模型的应用前景,综述了国内外构建花粉量产量预测模型的相关研究进展,发现在传统的气象产量预测统计模型中,引入花粉量因子,可以提高产量预测的准确性;基于花粉量监测技术的限制,花粉量产量预测模型更适用于同一生长季内单一作物,面积超过一定规模的种植区域的产量预测,有较好的应用前景;对于同一生长季,作物种类多、面积小、空间分散的种植区域来说,花粉量产量预测模型应用效果的好坏将主要取决于通过花粉量监测技术获得的花粉量数据的质量和数量。要使花粉量产量预测模型在中国得到很好的应用,需要建设完备的花粉监测网,提高花粉识别技术并推广应用,积累足够的花粉量数据以构建稳定而全面的产量预测模型。     

Comparative Biomass Production of Six Oat Varieties in a Saline Soil Ecology

Oat is widely used as a food source in human and animal diets. In China, supplementary cultivation of oat has recently been extended into marginal saline soils, due to its extensive use. The goal of the present study was to explore and compare the agronomic characteristics, biomass production, and distribution of oat in coastal saline soil. A single-factor randomized block design experiment with six naked and husked oat varieties [Bayou-1 (BU-1), Baiyan-2 (BY-2), Baiyan-7 (BY-7), Huawan-6 (HW-6), Huazao-2 (HZ-2), and Pin-16 (P-16)] as treatments was conducted in the Hebei Province of China. The growth period (77 to 88 days) of the six oat varieties were shorter in saline soil than their original breeding region. Moreover, the plant was shorter, with a lower grain number per plant and seed setting. Grain weight increased and no change was observed in the floret number in coastal saline soil relative to the original habitats. The BY-7 variety produced the highest biomass value of 7.0 t ha^?1 compared to the other five varieties. Compared with two active growth points, most varieties in the present study showed just one growth activity point between elongation and heading. Two fast growth periods for the six oat varieties were due to the high total leaf biomass growth rate (BGR) from elongation to heading and panicle BGR from heading to maturity. The biomass growth ratio (BGO) during elongation to heading was the largest among the four growth stages of the oat varieties. The number of kernels per spike decreased at the research site, but grain weight increased. Substantial differences were observed for growth periods, biomass, BGR, BGO, and other agronomic characters in the coastal saline soil of North China, but the BY-7 variety was the most suitable variety for the study site.     

环境监测数据处理的多元分析法与应用进展

本文综述了环境监测数据处理最强有力的工具——多元分析法在环境监测中的应用范围、特点和前景。并介绍了一些国内外应用实例。     

广义线性地统计模型在典型亚热带丘陵区数字土壤制图中的应用

数字土壤制图(DSM)在当前的应用越来越广泛,其方法主要包括环境相关模型、空间自相关模型,以及这些模型的混合模型。理论上,混合模型相对单一模型具有明显的优势。广义线性地统计模型(GLGM)也是一种混合模型,相对于最常用的混合模型——回归克里格(RK),又具有能加入随机效应来解决土壤变异的非平稳性等优势。然而,GLGM因计算繁琐等缺点,在国内外应用较少。本文以广西南宁高峰林场内一小面积(3.03km2)丘陵为研究区,以14个地形因子为预测变量,使用GLMM及其与普通克里格(OK)相结合的模型(GLGM),对土壤有机碳(SOC)、pH、黏粒和阳离子交换量(CEC)的空间分布进行预测,并与常用的多元线性回归(MLR)、地理加权回归(GWR)、回归森林(RF)、普通克里格(OK)、RK和广义可加模型(GAM)进行比较。结果表明：GLMM在预测黏粒上准确度较高;GLMM和GLGM在预测CEC上准确度中等;GLMM和GLGM在预测SOC和pH上准确度较低。综合线性回归模型的调整决定系数、块金效应和全局莫兰指数,本文认为,当土壤属性与环境变量具有较低的线性回归调整决定系数(即小于5%)、土壤属性具有较弱的空间自相关性(即块金效应大于71%)和较强的局部空间变异(即全局莫兰指数小于0.09)时,GLMM和GLGM具有较高的适用性,例如本文中的黏粒。反之,GLMM和GLGM的适用性不好,例如本文中的SOC和pH。鉴于土壤空间变异的高度异质性和多尺度性,本文认为GLMM和GLGM具有较好的应用前景。但是,在未来的研究中还需要进一步研究如何提高GLMM和GLGM的模拟效率。     

地表覆盖及生理生态因子对苹果树光合特性的影响

以9年生长枝富士苹果树为研究对象,利用LI-6400便携式光合测定系统监测不同地表覆盖模式下盛果期苹果树净光合速率、蒸腾速率日变化,并对与净光合速率和蒸腾速率相关的环境因子及生理子的相互关系进行分析.结果表明,清耕和生草处理的净光合速率(Pn)日变化为双峰型,地膜覆盖、秸秆覆盖和砂石覆盖的净光合速率(Pn)日变化进程为单峰型;蒸腾速率(Tr)日变化均为单峰型.净光合速率(Pn)与光合有效辐射(PAR)、气温(Ta)、叶温(Tl)均呈二次曲线关系;与大气相对湿度(RH)、气孔导度(Gs)呈显著正相关;与胞间CO2浓度(Ci)呈显著负相关.蒸腾速率(Tr)与光合有效辐射(PAR)为幂指数关系;与大气相对湿度(RH)呈二次曲线关系;与气温(Ta)、叶温(Tl)、气孔导度(Gs)呈显著正相关;与胞间CO2浓度(Gi)显著负相关.砂石覆盖和秸秆覆盖的净光合速率最大,不同覆盖模式下蒸腾速率日均值差异不显著,不同覆盖模式下苹果树的光合特性与各项环境因子和生理因子密切相关.     

可变电荷土壤表面酸碱性质与模型研究进展

可变电荷土壤表面酸碱性质是指土壤与质子的结合能力,主要包括表面质子反应活性位点密度Ds、电荷零点pH_(pzc)以及质子化、去质子平衡常数pK_a。表面酸碱性质对于评估土壤酸碱缓冲能力、揭露酸化机制有重要意义,也是探究阳离子和阴离子在土壤固液间的分配,控制微量元素移动性和生物有效性的重要土壤性质之一。本文综述了通过表面络合模型获取表面酸碱性质参数的方法以及研究复杂土壤体系的两种方法。电荷零点pH_(pzc)主要通过宏观滴定实验获得,但需要不断优化实验操作来减少无意义因素的影响。而表面质子反应活性位点密度D_s和质子化、去质子化平衡常数pK_a,则通常分别通过Gran函数和直线外推法计算等方法得到。目前,土壤活性成分金属氧化物、黏土矿物及腐殖质表面酸碱性质已通过表面络合模型得到广泛研究,研究方法和相关数据已逐渐趋于完善。而随着表面络合模型的发展,土壤混合体系表面酸碱性质的研究也在不断加深。组分添加法通过各组分表面性质的加和来预测质子在土壤和溶液中分配和形态,目前已从矿物聚合模型扩展到矿物-有机物聚合模型,因为有机物和矿物组分之间的相互作用对于研究相当重要。而广义复合法假设土壤表面性质均一,依据实验吸附数据和表面积拟合获得表面酸碱性质,现在已发展为n-site/n-p Ka,并建立了一种通过土壤化学性质预测表面酸碱性质广义回归方程。最后,文章探讨了表面络合模型相关研究应该解决的科学问题,以及土壤表面酸碱性质研究在未来的趋势。     

Determination of Surrogate Indicators for Phosphorus and Solids in Urban Stormwater: Application of Multivariate Data Analysis Techniques

Solids and phosphorus found within urban stormwater have the potential to cause environmental damage to ecological systems in receiving waters. The evaluation of these pollutants in urban stormwater is usually undertaken by physico-chemical monitoring programs which sample streamflow for laboratory assessment. In this study, data from two such monitoring programs have been examined for the catchment characteristics which influence solids and phosphorus discharge behaviour and the potential for the use of surrogate indicators to predict streamflow concentrations. The study involved partitioning of the components on the basis of the dissolved and particulate fractions. Investigation of the physical and chemical behaviour of solids and phosphorus by univariate and multivariate data analysis techniques led to the identification of interrelationships among the measured parameters. Thus, using turbidity and conductivity, relationships were developed for suspended and dissolved solids. Similarly, relationships were developed for dissolved and particulate phosphorus using suspended and dissolved solids. These relationships have the potential to enhance rapid generation of vital information from site-based measurements and to reduce the requirements for laboratory-based analysis of indicator concentrations in urban stormwater.     

基于多元数据的云南省水土保持区划方法研究

为了增强全国水土保持三级区划在云南省的适用性和指导性,更有效地开展水土保持预防治理工作。针对云南省开展水土保持四级区划,选取自然、社会经济、土地利用、水土保持4类要素的86个指标构建了云南省水土保持区划指标体系,通过SPSS软件对指标体系进行主成分分析和聚类分析,结合GIS软件将聚类分析结果绘制成自然要素分布图、社会经济要素分布图、土地利用要素分布图、水土保持要素分布图,并对这4个要素分布图进行空间叠置,初步完成了云南省水土保持四级分区工作。该方法大大提高水土保持区划的效率和准确性,以期为不同层次水土保持区划工作提供借鉴。     

松嫩平原气候数据空间分布模型及栅格化信息系统的建立

在GIS支持下,以松嫩平原DEM为基础,建立经度、纬度、海拔高度等大地形因子数据库及坡度、坡向、遮蔽度等小地形因子数据库。利用松嫩平原各县市1961-2000年平均太阳辐射、气温、降水和相对湿度数据,采用三维二次趋势面模型进行逐步回归,构建松嫩平原逐月主要气候要素与宏观地理因子的数学模型。完成松嫩平原各气候要素空间分布的综合数学模型,并利用GIS平台建立空间分辨率为1km×1km的松嫩平原主要气候要素的网格化数字专题图。     

Another Look at Bayesian Analysis of AMMI Models for Genotype-Environment Data

Linear–bilinear models are frequently used to analyze two-way data such as genotype-by-environment data. A well-known example of this class of models is the additive main effects and multiplicative interaction effects model (AMMI). We propose a new Bayesian treatment of such models offering a proper way to deal with the major problem of overparameterization. The rationale is to ignore the issue at the prior level and apply an appropriate processing at the posterior level to be able to arrive at easily interpretable inferences. Compared to previous attempts, this new strategy has the great advantage of being directly implementable in standard software packages devoted to Bayesian statistics such as WinBUGS/OpenBUGS/JAGS. The method is assessed using simulated datasets and a real dataset from plant breeding. We discuss the benefits of a Bayesian perspective to the analysis of genotype-by-environment interactions, focusing on practical questions related to general and local adaptation and stability of genotypes. We also suggest a new solution to the estimation of the risk of a genotype not exceeding a given threshold.