Using network models to approximate spatial point-process models

Spatial effects are fundamental to ecological and epidemiological systems, yet the incorporation of space into models is potentially complex. Fixed-edge network models (i.e. networks where each edge has the same fixed strength of interaction) are widely used to study spatial processes but they make simplistic assumptions about spatial scale and structure. Furthermore, it can be difficult to parameterize such models with empirical data. By comparison, spatial point-process models are often more realistic than fixed-edge network models, but are also more difficult to analyze. Here we develop a moment closure technique that allows us to define a fixed-edge network model which predicts the prevalence and rate of epidemic spread of a continuous spatial point-process epidemic model. This approach provides a systematic method for accurate parameterization of network models using data from continuously distributed populations (such as data on dispersal kernels). Insofar as point-process models are accurate representations of real spatial biological systems, our example also supports the view that network models are realistic representations of space.     

Fitting conditional survival models to meta-analytic data by using a transformation toward mixed-effects models

Summary .   Frailty models are widely used to model clustered survival data. Classical ways to fit frailty models are likelihood-based. We propose an alternative approach in which the original problem of "fitting a frailty model" is reformulated into the problem of "fitting a linear mixed model" using model transformation. We show that the transformation idea also works for multivariate proportional odds models and for multivariate additive risks models. It therefore bridges segregated methodologies as it provides a general way to fit conditional models for multivariate survival data by using mixed models methodology. To study the specific features of the proposed method we focus on frailty models. Based on a simulation study, we show that the proposed method provides a good and simple alternative for fitting frailty models for data sets with a sufficiently large number of clusters and moderate to large sample sizes within covariate-level subgroups in the clusters. The proposed method is applied to data from 27 randomized trials in advanced colorectal cancer, which are available through the Meta-Analysis Group in Cancer.     

Likelihood-ratio tests for hidden Markov models

We consider hidden Markov models as a versatile class of models for weakly dependent random phenomena. The topic of the present paper is likelihood-ratio testing for hidden Markov models, and we show that, under appropriate conditions, the standard asymptotic theory of likelihood-ratio tests is valid. Such tests are crucial in the specification of multivariate Gaussian hidden Markov models, which we use to illustrate the applicability of our general results. Finally, the methodology is illustrated by means of a real data set.     

一种模型模拟结果的统计检验方法

模拟结果的准确性是衡量生态学模型是否成功的关键,但采用统计学方法判别模型模拟结果与观察值相符程度的报道较少。根据两个直线回归方程能否合并为一个方程的统计学检验方法,提出了通过检验观察值与模拟值直线回归方程和1∶1直线方程截距与斜率是否相同,进而在统计显著水平上判断生态学模型模拟值与观察值一致性的统计学检验方法。数据检验表明,此方法可以较好解决判断生态学模型模拟结果准确性的问题。     

Semiparametric models for missing covariate and response data in regression models

We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a sensitivity analysis for model misspecification of the missing covariate distribution and/or missing data mechanism. The semiparametric model consists of a generalized additive model (GAM) for the covariate distribution and/or missing data mechanism. Penalized regression splines are used to express the GAMs as a generalized linear mixed effects model, in which the variance of the corresponding random effects provides an intuitive index for choosing between the semiparametric and parametric model. Maximum likelihood estimates are then obtained via the EM algorithm. Simulations are given to demonstrate the methodology, and a real data set from a melanoma cancer clinical trial is analyzed using the proposed methods.     

Flexible parametric measurement error models

Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect, the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference, we propose using flexible parametric models that can accommodate departures from standard parametric models. We use mixtures of normals for this purpose. We study two cases in detail: a linear errors-in-variables model and a change-point Berkson model.     

On the mechanistic underpinning of discrete-time population models with Allee effect

The Allee effect means reduction in individual fitness at low population densities. There are many discrete-time population models with an Allee effect in the literature, but most of them are phenomenological. Recently, Geritz and Kisdi [2004. On the mechanistic underpinning of discrete-time population models with complex dynamics. J. Theor. Biol. 228, 261-269] presented a mechanistic underpinning of various discrete-time population models without an Allee effect. Their work was based on a continuous-time resource-consumer model for the dynamics within a year, from which they derived a discrete-time model for the between-year dynamics. In this article, we obtain the Allee effect by adding different mate finding mechanisms to the within-year dynamics. Further, by adding cannibalism we obtain a higher variety of models. We thus present a generator of relatively realistic, discrete-time Allee effect models that also covers some currently used phenomenological models driven more by mathematical convenience.     

Econometric models for biohydrogen development

Biohydrogen is considered as an attractive clean energy source due to its high energy content and environmental-friendly conversion. Analyzing various economic scenarios can help decision makers to optimize development strategies for the biohydrogen sector. This study surveys econometric models of biohydrogen development, including input-out models, life-cycle assessment approach, computable general equilibrium models, linear programming models and impact pathway approach. Fundamentals of each model were briefly reviewed to highlight their advantages and disadvantages. The input-output model and the simplified economic input-output life-cycle assessment model proved most suitable for economic analysis of biohydrogen energy development. A sample analysis using input-output model for forecasting biohydrogen development in the United States is given.     

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。     

On the mechanistic underpinning of discrete-time population models with complex dynamics

We present a mechanistic underpinning for various discrete-time population models that can produce limit cycles and chaotic dynamics. Specific examples include the discrete-time logistic model and the Hassell model, which for a long time eluded convincing mechanistic interpretations, and also the Ricker- and Beverton-Holt models. We first formulate a continuous-time resource consumption model for the dynamics within a year, and from that we derive a discrete-time model for the between-year dynamics. Without influx of resources from the outside into the system, the resulting between-year dynamics is always overcompensating and hence may produce complex dynamics as well as extinction in finite time. We recover a connection between various standard types of continuous-time models for the resource dynamics within a year on the one hand and various standard types of discrete-time models for the population dynamics between years on the other. The model readily generalizes to several resource and consumer species as well as to more than two trophic levels for the within-year dynamics.     

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km²) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.     

Structural time series models with feedback mechanisms

Structural time series models have applications in many different fields such as biology, economics, and meteorology. A structural times series model can be represented as a state-space model where the states of the system represent the unobserved components and the structural parameters have clear interpretations. This paper introduces a class of structural time series models that incorporate feedback from the latent components of the history. An iterative procedure is proposed for estimation. These models allow flexible and robust feedback mechanisms, have clear interpretations, and have a computationally efficient estimation procedure. They are applied to hormone data to characterize hormone secretion and to explore a potential feedback mechanism.     

Neural hierarchical models of ecological populations

Neural networks are increasingly being used in science to infer hidden dynamics of natural systems from noisy observations, a task typically handled by hierarchical models in ecology. This article describes a class of hierarchical models parameterised by neural networks – neural hierarchical models. The derivation of such models analogises the relationship between regression and neural networks. A case study is developed for a neural dynamic occupancy model of North American bird populations, trained on millions of detection/non‐detection time series for hundreds of species, providing insights into colonisation and extinction at a continental scale. Flexible models are increasingly needed that scale to large data and represent ecological processes. Neural hierarchical models satisfy this need, providing a bridge between deep learning and ecological modelling that combines the function representation power of neural networks with the inferential capacity of hierarchical 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.     

物候模型研究进展

近年来随着全球气候变暖,物候提前,物候学的研究越来越受到人们的关注.通过建立物候模型使物候期的预知成为可能,从而为生产实践活动提供依据和指导.本文探讨了物候模型研究的意义,总结了影响植物和昆虫物候的温度、水分、光和养分等主要环境因子的作用.根据国内外物候模型的研究现状,重点介绍了作物、树木、植被和昆虫4类物候模型的研究内容和进展.作物物候模型注重生理生态过程;树木物候模型以统计方法为主,但近期也有尝试将激素水平作为物候的决定因素;植被物候模型以遥感技术的应用为发展趋势;昆虫物候模型则进一步对发育起点的确定和对温度因子的修正,GIS的引入将昆虫物候模型的应用范围扩大.最后指出了目前物候模型研究中存在的问题.     

Laplace approximation in measurement error models

Likelihood analysis for regression models with measurement errors in explanatory variables typically involves integrals that do not have a closed-form solution. In this case, numerical methods such as Gaussian quadrature are generally employed. However, when the dimension of the integral is large, these methods become computationally demanding or even unfeasible. This paper proposes the use of the Laplace approximation to deal with measurement error problems when the likelihood function involves high-dimensional integrals. The cases considered are generalized linear models with multiple covariates measured with error and generalized linear mixed models with measurement error in the covariates. The asymptotic order of the approximation and the asymptotic properties of the Laplace-based estimator for these models are derived. The method is illustrated using simulations and real-data analysis.     

红树林湿地模型研究进展

红树林是位于海岸潮间带的森林湿地生态系统,具有抗风削浪、保护堤岸、封存CO2缓解全球气候变化等多种功能。然而,由于地理位置及生存环境特殊,红树林湿地模型的研究相对内陆湿地滞后。为了推动红树林湿地模型的研究,本文将目前常见红树林湿地模型按功能划分为植被生长生产模型、水文模型、物流能流模型三类,以阐述相关模型研究的进展,并对目前红树林湿地模型研究提出几点看法:(1)现有红树林湿地模型在不同区域、尺度下运行的有效性有待进一步验证;(2)国内对红树林生态系统服务及植被恢复工作相关模型研究较少,今后需加强该方面的研究。     

物种分布模型理论研究进展

利用物种分布模型估计物种的真实和潜在分布区,已成为区域生态学与生物地理学中非常活跃的研究领域。然而,到目前为止,这项技术的理论基础仍然存在不足之处,一些关键的生态过程未能被有效纳入到物种分布模型的理论框架中,从而为解释物种分布模型预测的结果带来了诸多困惑。鉴于此,总结了物种分布模型的理论基础;系统探讨了物种分布模型与物种分布区的关系;特别指出了物种分布模型研究中存在的理论问题;重点阐述了物种分布模型未来的发展方向。研究认为,物种分布模型与生态位理论、源-库理论、种群动态理论、集合种群理论、进化理论等具有重要的联系;正确理解物种分布模型的预测结果与物种分布区的关系,有赖于对影响物种分布的3个主要因素(环境条件、物种相互作用与物种迁移能力)做出定量的分离;目前物种分布模型主要存在的问题是未能将物种的相互作用和物种的迁移能力有效纳入到模型的构建过程中;未来物种分布模型的发展应该加强模型背后理论框架的研究,并进一步加强整合物种相互作用过程、种群动态过程、迁移过程和物种进化过程等内容。研究还认为,从更高的理论层次模拟功能群和群落结构将是未来物种分布模型的重要发展方向。     

Animal infection models using non-mammals

The use of non-human animal models for infection experiments is important for investigating the infectious processes of human pathogenic bacteria at the molecular level. Mammals, such as mice and rabbits, are also utilized as animal infection models, but large numbers of animals are needed for these experiments, which is costly, and fraught with ethical issues. Various non-mammalian animal infection models have been used to investigate the molecular mechanisms of various human pathogenic bacteria, including Staphylococcus aureus, Streptococcus pyogenes, and Pseudomonas aeruginosa. This review discusses the desirable characteristics of non-mammalian infection models and describes recent non-mammalian infection models that utilize Caenorhabditis elegans, silkworm, fruit fly, zebrafish, two-spotted cricket, hornworm, and waxworm.     

Conventional and newly developed bioheat transport models in vascularized tissues: A review

Heat transfer in a biological system is a complex process and its analysis is difficult. Heterogeneous vascular architecture, blood flow in the complex network of arteries and veins, varying metabolic heat generation rates and dependence of tissue properties on its physiological condition contribute to this complexity. The understanding of heat transfer in human body is important for better insight of thermoregulatory mechanism and physiological conditions. Its understanding is also important for accurate prediction of thermal transport and temperature distribution during biomedical applications. During the last three decades, many attempts have been made by researchers to model the complex thermal behavior of the human body. These models, viz., blood perfusion, countercurrent, thermal phase-lag, porous-media, perturbation, radiation, etc. have their corresponding strengths and limitations. Along with their biomedical applications, this article reviews various contextual issues associated with these models. After brief discussion of early bioheat models, the newly developed bioheat models are discussed in detail. Dependence of these models on biological properties, viz., thermophysical and optical properties are also discussed.