森林动态模型概论

讨论了模拟森林林分动态变化的模型,并把模型分为森林生长模型和演替模型。森林生长模型包括：全林分模型、林分级模型和单木模型;演替模型包括马尔可夫类模型和林窗模型。文中给出了演替模型的基本原理和适用性,在比较早期和最新发展的林窗模型后,叙述了林窗模型的新进展。生长和演替模型的结构和数据要求不同决定了它们的在时间和空间上的适应性,最后指出模型将向综合总体方向发展     

前列腺癌鼠模型

前列腺癌鼠模型是研究前列腺癌的重要工具,目前常见以下4类：自发和诱发鼠模型,异种移植鼠模型,转基因鼠模型和基因敲除鼠模型。简要综述了前列腺癌鼠模型的研究进展。     

森林动态模型概论

讨论了模拟森林林分动态变化的模型,并把模型分为森林生长模型和演替模型。森林生长模型包括：全林分模型、林分级模型和单木模型;演替模型包括马尔可夫类模型和林窗模型。文中给出了演替模型的基本原理和适用性,在比较早期和最新发展的林窗模型后,叙述了林窗模型的新进展。生长和演替模型的结构和数据要求不同决定了它们的在时间和空间上的适应性,最后指出模型将向综合总体方向发展。     

壤中流模型研究的现状及存在问题

对国内外壤中流模型与模拟进行了较为系统的介绍,并针对这些模型提出了一种壤中流模型分类的方法,即根据模型所依据的主要原理将壤中流模型分为三大类：１）Ｒｉｃｈａｒｄｓ模型;２）动力波模型;３）贮水泄流模型．Ｒｉｃｈａｒｄｓ模型又可分为一维Ｒｉｃｈａｒｄｓ模型、二维Ｒｉｃｈａｒｄｓ模型和三维Ｒｉｃｈａｒｄｓ模型;贮水泄流模型又可分为动力贮水泄流模型和Ｂｏｕｓｉｎｅｓｑ贮水泄流模型．同时,将这３类模型进行对比,指出了它们各自优点和不足．     

昆虫物候模型研究进展

物候是昆虫的重要生物学性状之一。物候模型预测昆虫发育事件的时间,在种群动态、物种分布和进化动态等科学研究以及农林业生产中具有重要作用。本文回顾了常见的物候模型及在昆虫学研究上的应用,包括热性能曲线、生物物理模型、基于概率的模型、分布时滞模型、发育进度曲线、物候匹配模型和物候变迁模型。     

运动昆虫密度和疾病传染数量估计的两类模型

根据推导,得到用于运动昆虫密度和疾病接触传染数量估计的二维和三维空间模型．两类模型分别适用于爬行昆虫和飞行昆虫的种群密度估计．二维空间模型可用于描述昆虫疾病的接触传染数量,针对不同的用途,将两类基本模型进行了数种拓广.两类模型的建立,为运动昆虫的密度估计及疾病流行研究提供了重要的定量工具．     

Relationships among recent models for insect population dynamics with variable rates of development

A classification scheme for those population models which allow variation in development rates is proposed, based on two ways of modifying standard age-structured models. The resulting classes of models are termed development index models and sojourn time models. General formulations for the two classes of models are developed from two basic balance equations, and numerous specific models from the literature are shown to fit into the scheme. Concepts from competing risks theory are shown to be important in understanding the interplay between mortality and maturation. Relationships among the classes are investigated both for the most general forms of the models and for the simpler forms often used. The scheme can provide guidance in developing appropriate insect population models for specific modelling situations.Contribution 3878871     

Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences

Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior.     

A complete enumeration and classification of two-locus disease models

There are 512 two-locus, two-allele, two-phenotype, fully penetrant disease models. Using the permutation between two alleles, between two loci, and between being affected and unaffected, one model can be considered to be equivalent to another model under the corresponding permutation. These permutations greatly reduce the number of two-locus models in the analysis of complex diseases. This paper determines the number of nonredundant two-locus models (which can be 102, 100, 96, 51, 50, or 58, depending on which permutations are used, and depending on whether zero-locus and single-locus models are excluded). Whenever possible, these nonredundant two-locus models are classified by their property. Besides the familiar features of multiplicative models (logical AND), heterogeneity models (logical OR), and threshold models, new classifications are added or expanded: modifying-effect models, logical XOR models, interference and negative interference models (neither dominant nor recessive), conditionally dominant/recessive models, missing lethal genotype models, and highly symmetric models. The following aspects of two-locus models are studied: the marginal penetrance tables at both loci, the expected joint identity-by-descent (IBD) probabilities, and the correlation between marginal IBD probabilities at the two loci. These studies are useful for linkage analyses using single-locus models while the underlying disease model is two-locus, and for correlation analyses using the linkage signals at different locations obtained by a single-locus model.     

Three dimensional printing as an effective method of producing anatomically accurate models for studies in thermal ecology

Hollow copper models painted to match the reflectance of the animal subject are standard in thermal ecology research. While the copper electroplating process results in accurate models, it is relatively time consuming, uses caustic chemicals, and the models are often anatomically imprecise. Although the decreasing cost of 3D printing can potentially allow the reproduction of highly accurate models, the thermal performance of 3D printed models has not been evaluated. We compared the cost, accuracy, and performance of both copper and 3D printed lizard models and found that the performance of the models were statistically identical in both open and closed habitats. We also find that 3D models are more standard, lighter, durable, and inexpensive, than the copper electroformed models.     

土壤-植物-大气连续体水热、CO2通量估算模型研究进展

土壤-植物-大气连续体(SPAC)水热、CO2通量的准确估算对理解陆地和大气的物质和能量交换过程有着重要意义.重点阐述了基于过程的土壤-植物-大气连续体水热、CO2通量模型,综述了统计模型、综合模型及基于遥感的模型的发展过程.其中水热通量统计模型包括基于温度和湿度以及基于温度和辐射的方法;CO2通量统计模型包括基于气候因子或蒸散因子以及基于光能利用率的方法.水热通量过程模型包括大叶、双源、多源和多层的水热传输物理模型;CO2通量过程模型包括叶片尺度及由大叶、双叶和多层方法扩展到冠层尺度的生理生态模型以及光合-蒸腾耦合模型.综合模型包括生物物理模型、生物化学模型和生物地理模型.统计模型形式简单,资料易得,对大范围的水热通量模拟具有指导意义;过程模型准确的揭示了水热和CO2通量传输的物理和生理过程,是大尺度综合模型的基础.未来生态系统水热、CO2通量估算模型将集成各种技术手段进行多尺度网络观测和大尺度机理模拟.     

Dynamic conditionally linear mixed models for longitudinal data

We develop a new class of models, dynamic conditionally linear mixed models, for longitudinal data by decomposing the within-subject covariance matrix using a special Cholesky decomposition. Here 'dynamic' means using past responses as covariates and 'conditional linearity' means that parameters entering the model linearly may be random, but nonlinear parameters are nonrandom. This setup offers several advantages and is surprisingly similar to models obtained from the first-order linearization method applied to nonlinear mixed models. First, it allows for flexible and computationally tractable models that include a wide array of covariance structures; these structures may depend on covariates and hence may differ across subjects. This class of models includes, e.g., all standard linear mixed models, antedependence models, and Vonesh-Carter models. Second, it guarantees the fitted marginal covariance matrix of the data is positive definite. We develop methods for Bayesian inference and motivate the usefulness of these models using a series of longitudinal depression studies for which the features of these new models are well suited.     

Calculating Ordinal Regression Models in SAS and S‐Plus

Although a number of regression models for ordinal responses have been proposed, these models are not widely known and applied in epidemiology and biomedical research. Overviews of these models are either highly technical or consider only a small part of this class of models so that it is difficult to understand the features of the models and to recognize important relations between them. In this paper we give an overview of logistic regression models for ordinal data based upon cumulative and conditional probabilities. We show how the most popular ordinal regression models, namely the proportional odds model and the continuation ratio model, are embedded in the framework of generalized linear models. We describe the characteristics and interpretations of these models and show how the calculations can be performed by means of SAS and S‐Plus. We illustrate and compare the methods by applying them to data of a study investigating the effect of several risk factors on diabetic retinopathy. A special aspect is the violation of the usual assumption of equal slopes which makes the correct application of standard models impossible. We show how to use extensions of the standard models to work adequately with this situation.     

Mixed-effect hybrid models for longitudinal data with nonignorable dropout

Summary .  Selection models and pattern-mixture models are often used to deal with nonignorable dropout in longitudinal studies. These two classes of models are based on different factorizations of the joint distribution of the outcome process and the dropout process. We consider a new class of models, called mixed-effect hybrid models (MEHMs), where the joint distribution of the outcome process and dropout process is factorized into the marginal distribution of random effects, the dropout process conditional on random effects, and the outcome process conditional on dropout patterns and random effects. MEHMs combine features of selection models and pattern-mixture models: they directly model the missingness process as in selection models, and enjoy the computational simplicity of pattern-mixture models. The MEHM provides a generalization of shared-parameter models (SPMs) by relaxing the conditional independence assumption between the measurement process and the dropout process given random effects. Because SPMs are nested within MEHMs, likelihood ratio tests can be constructed to evaluate the conditional independence assumption of SPMs. We use data from a pediatric AIDS clinical trial to illustrate the models.     

Generalizability of neotropical bird abundance and richness models

Predicting the consequences of land-cover change on tropical biotas is a pressing task. However, testing the applicability of models developed with data from one region to another region has rarely been done. Bird faunas were sampled along 3.0-km routes in southern Costa Rica (Coto Brus) to develop statistical models to describe the abundance and richness of groups as a function of land-cover characteristics. The relative value of the land-cover models was assessed by comparing them with null models. The generalizability of the models was tested with data from north-western Costa Rica (Monteverde) to determine whether the models were applicable to another area that has undergone significant land-cover change in the last 60 years. The richness and abundance of understory, open-country and edge non-insectivore groups showed clear relationships with land-cover variables, and the land-cover models had lower prediction errors than the null models for Coto Brus. With one exception, useful models for canopy birds, edge insectivores and hummingbirds could not be developed. The land-cover models of abundance of canopy insectivores, understory insectivores and non-insectivores, and edge non-insectivores were generalizable to Monteverde whereas the land-cover models of abundance of open-country birds and species richness for any of the groups were not better than null models for Monteverde. The results indicate that land-cover models that describe the abundance or richness of various bird groups provide useful predictions in the area where the data were collected and that models of abundance of some canopy, understory and edge birds may perform well in areas that are similar in elevation, life zones and land use to the area from which data were collected. Land-cover models of the abundance of other groups, and of the richness of the majority of groups, may be less generalizable to other areas, or it may be difficult to develop models at all.     

Comparative modeling and protein-like features of hydrophobic-polar models on a two-dimensional lattice

Lattice models of proteins have been extensively used to study protein thermodynamics, folding dynamics, and evolution. Our study considers two different hydrophobic-polar (HP) models on the 2D square lattice: the purely HP model and a model where a compactness-favoring term is added. We exhaustively enumerate all the possible structures in our models and perform the study of their corresponding folds, HP arrangements in space and shapes. The two models considered differ greatly in their numbers of structures, folds, arrangements, and shapes. Despite their differences, both lattice models have distinctive protein-like features: (1) Shapes are compact in both models, especially when a compactness-favoring energy term is added. (2) The residue composition is independent of the chain length and is very close to 50% hydrophobic in both models, as we observe in real proteins. (3) Comparative modeling works well in both models, particularly in the more compact one. The fact that our models show protein-like features suggests that lattice models incorporate the fundamental physical principles of proteins. Our study supports the use of lattice models to study questions about proteins that require exactness and extensive calculations, such as protein design and evolution, which are often too complex and computationally demanding to be addressed with more detailed models.     

异质种群动态模型:破碎化景观动态模拟的新途径

景观破碎化导致物种以异质种群方式存活,使得基于异质种群动态模拟破碎化景观动态成为可能。异质种群动态模型的发展为景观动态模拟奠定了良好基础。根据空间处理方式的不同,异质种群模型可分为三大类,可不同程度地用于描述破碎化景观动态。(1)空间不确定异质种群模型,假定所有局域种群间均等互联,模型中不包含空间信息,仅能用于景观斑块动态描述;(2)空间确定异质种群模型,假设局域种群在二维空间上以规则格子形式排列,是一种准现实的空间处理方式,可用于景观动态的简单描述;(3)空间现实异质种群模型,包含了破碎化景观中局域种群的几何特征,可直接用于真实景观动态的模拟研究。空间现实的和基于个体的异质种群模型不但是未来异质种群模型发展的主流,也将成为未来破碎化景观动态研究的重要工具。为了更加准确完整地描述破碎化景观动态,不但应该综合运用已有的各种异质种群模型方法,更要引进新模型来刎画多物种、多变量、高维度、复杂连接的破碎化景观格局与过程。     

Modeling nonstationary longitudinal data

An important theme of longitudinal data analysis in the past two decades has been the development and use of explicit parametric models for the data's variance-covariance structure. A variety of these models have been proposed, of which most are second-order stationary. A few are flexible enough to accommodate nonstationarity, i.e., nonconstant variances and/or correlations that are not a function solely of elapsed time between measurements. We review five nonstationary models that we regard as most useful: (1) the unstructured covariance model, (2) unstructured antedependence models, (3) structured antedependence models, (4) autoregressive integrated moving average and similar models, and (5) random coefficients models. We evaluate the relative strengths and limitations of each model, emphasizing when it is inappropriate or unlikely to be useful. We present three examples to illustrate the fitting and comparison of the models and to demonstrate that nonstationary longitudinal data can be modeled effectively and, in some cases, quite parsimoniously. In these examples, the antedependence models generally prove to be superior and the random coefficients models prove to be inferior. We conclude that antedependence models should be given much greater consideration than they have historically received.     

Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy

Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series Targeted. Among the uses of such a taxonomy are that existing models can be embedded in the context of possible other models, new models can be derived, models can be compared, and the relation of statistical models to theories of causality can be specified. Sample models are depicted, and parameters of interest are indicated. For two new models, empirical data examples are provided from research on the development of aggression in adolescents.