外来有害生物风险评估方法研究进展

外来有害生物给生态环境、农业生产、人类健康带来的影响和造成的经济损失越来越受到人们的关注。与此同时,对外来有害生物风险评估方法的研究及其应用也在逐步深入。通过对多指标综合评估法、农业气候相似距法、地理信息系统、生态气候模型评价、模糊综合评判等方法的分析,概述了外来有害生物风险评估方法的含义和利弊,探讨了不同外来物种在不同环境条件下的适生性,综述了国内外评估方法应用的研究进展,对该研究领域的发展趋势进行了展望。     

物种分布模型在海洋潜在生境预测的应用研究进展

海洋生物的栖息分布与环境要素的关联性一直是海洋生态学研究的热点之一.近年来,物种分布模型被广泛应用于预测海洋物种分布、潜在适宜性生境评价等研究,为保护海洋生物多样性、防治外来物种入侵及制定渔业管理措施等提供了一条有效途径.物种分布模型主要包括生境适宜性指数模型、机理模型和统计模型.本文对物种分布模型的理论基础进行了归纳和总结,回顾了物种分布模型在预测海洋物种潜在地理分布研究中的开发与应用,重点介绍了不同类型统计模型在海洋物种潜在分布预测中的研究实例.比较各种选取变量和模型验证方法,认为赤池信息准则对于选取模型变量具有优势,Kappa系数和受试者操作特征曲线下面积在验证模型精度中应用最广泛.阐述了物种分布模型存在的问题及未来发展趋势,随着海洋生物生理机制研究的进一步深入,机理模型将是今后物种分布模型发展的重点.     

中国植物分布模拟研究现状

在过去的20年里, 物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例, 利用中国知网、维普网以及Web of Science文献数据库的检索与统计, 分析了2000-2018年间, 中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献, 分析表明2011年以来中国的物种分布模型应用发展迅速, 且以最近5年最为迅猛, 在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中, 应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据, 另一半研究不仅包含气候数据还包括地形与土壤等数据; 环境及物种数据的来源多样, 国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种, 既有木本植物(52.7%), 也有草本植物(41.8%), 其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测, 以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究, 与模拟物种适生区与推广种植等应用研究并重, 物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用, 多物种、多模型和多来源数据共同参与模拟与比较, 开发新的机理性物种分布模型, 拓展新的物种分布模拟应用领域, 是今后研究的重点发展方向。     

提高生态位模型转移能力来模拟入侵物种 的潜在分布

生态位模型利用物种分布点所关联的环境变量去推算物种的生态需求, 模拟物种的分布。在模拟入侵物种分布时, 经典生态位模型包括模型构建于物种本土分布地, 然后将其转移并投射至另一地理区域, 来模拟入侵物种的潜在分布。然而在模型运用时, 出现了模型的转移能力较低、模拟的结果与物种的实际分布不相符的情况, 由此得出了生态位漂移等不恰当的结论。提高生态位模型的转移能力, 可以准确地模拟入侵物种的潜在分布, 为入侵种的风险评估提供参考。作者以入侵种茶翅蝽(Halyomorpha halys)和互花米草(Spartina alterniflora)为例, 从模型的构建材料(即物种分布点和环境变量)入手, 全面阐述提高模型转移能力的策略。在构建模型之前, 需要充分了解入侵物种的生物学特性、种群平衡状态、本土地理分布范围及物种的生物历史地理等方面的知识。在模型构建环节上, 物种分布点不仅要充分覆盖物种的地理分布和生态空间的范围, 同时要降低物种采样点偏差; 环境变量的选择要充分考虑其对物种分布的限制作用、各环境变量之间的空间相关性, 以及不同地理种群间生态空间是否一致, 同时要降低环境变量的空间维度; 模型构建区域要真实地反映物种的地理分布范围, 并考虑种群的平衡状态。作者认为, 在生态位保守的前提下, 如果模型是构建在一个合理方案的基础上, 生态位模型的转移能力是可以保证的, 在以模型转移能力较低的现象来阐述生态位分化时需要引起注意。     

使用基因组数据进行贝叶斯物种分化时间估计

随着测序技术的发展,基因组数据呈井喷式增长.大量的基因组数据使得我们可以非常准确的估计出进化距离,即进化速率和物种分化时间的乘积.在很多情况下,我们想要了解的是物种分化的绝对时间.然而分子数据并没有分别提供进化速率和分化时间的信息,我们可以使用贝叶斯方法借助化石、先验等外部信息进行估计.近年来,进化模型和计算方法方面研究取得的进展使得我们可以在复杂模型下分析多基因数据.能够分析复杂模型的贝叶斯MCMC方法也成为了主流的物种分化时间估计方法.本文主要介绍贝叶斯物种分化时间估计的框架以及相关工作近年来的进展.     

中国植物分布模拟研究现状

在过去的20年里,物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例,利用中国知网、维普网以及Web of Science文献数据库的检索与统计,分析了2000–2018年间,中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献,分析表明2011年以来中国的物种分布模型应用发展迅速,且以最近5年最为迅猛,在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中,应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据,另一半研究不仅包含气候数据还包括地形与土壤等数据;环境及物种数据的来源多样,国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种,既有木本植物(52.7%),也有草本植物(41.8%),其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测,以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究,与模拟物种适生区与推广种植等应用研究并重,物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用,多物种、多模型和多来源数据共同参与模拟与比较,开发新的机理性物种分布模型,拓展新的物种分布模拟应用领域,是今后研究的重点发展方向。     

网络理论在入侵生物学中的应用研究现状

生物入侵是一个动态有序的过程,其发生和危害存在异质性,通常由来源地、入侵地和它们之间的连接构成的系统中的自然、生物、社会等因素所决定。网络理论是研究复杂系统的一种新方法,本质是从复杂的信息中抽象出规律、揭示系统的结构特征共性。近20年,网络理论已被应用于生物入侵研究。本研究综述了网络理论在生物入侵研究中的应用进展,明确了主要的研究方向和前沿热点,认为:2000年以来国际上已开展的研究集中在评估外来物种入侵风险和入侵后对生态系统影响2个方面;外来物种随运输网络入侵的风险评估和景观连接性对入侵物种扩散的影响、外来物种入侵对本地物种间互作网络的影响及生态群落可入侵性是网络理论应用的热点;研究热点具有明显的时间发展特征,2013年以前多是对生态系统的影响,近10年来主要是风险评估。我国利用网络理论研究外来物种入侵较少且集中于对生态系统的危害,未来应加强对外来物种的时空定量传入和扩散风险评估,为我国制定和提升外来入侵物种早期监测预警、阻止新的入侵、抑制进一步扩散的管理措施提供依据。     

鹿科动物MHC的基因组学、多态性及与性状的相关性

近年来主要组织相容性复合体(MHC)基因已经成为保护遗传学和分子生态学重要的遗传标记.鹿科动物是很多生态系统中的关键物种,也包含很多重要的经济物种和濒危物种.因此,鹿科动物的MHC研究可为种群遗传结构、进化、遗传多样性和种群生存力评估、疾病风险评估、抗病力的定向选育等领域的研究提供新的视角.本文从MHC的基因组结构、多态性,与抗病、抗寄生虫的关系,与鹿角生长的关系等方面,综述了20年来鹿科动物MHC的研究成果,并为今后的研究提出了思路.     

虚拟物种的基本原理及其在物种分布模型评估中的应用

物种分布模型是建立在物种出现或缺失数据的基础上,但可获得的真实分布数据存在着各种各样的缺点(如:物种识别错误、坐标错误、抽样偏差、数据缺失等),影响着物种分布模型的预测性能、稳定性及应用,因此使用物种真实分布数据评估物种分布模型将带来很大的不确定性。为避免这种不确定性,越来越多的研究使用虚拟物种来评价物种分布模型的性能,评估新方法的优劣。虚拟物种是一种建立在真实(或虚拟)地理信息系统下人工生命,是简化和抽象的物种,它通过模拟物种对环境变量的响应关系,评估物种在不同环境变量下的出现概率,人为地给出虚拟的物种分布数据。虚拟物种具有数据容易获得、数据质量可控、避免过度模拟等优势,目前它被广泛用于评估物种特性、抽样偏差、地理信息、出现/缺失标准等对物种分布模型性能的影响。虚拟物种是大尺度研究中不可或缺的重要工具,有利于解决真实数据未能解决的科学问题。常用的构成算法有求和法、求积法和综合法,但这些方法可能存在补偿效应,扩大了物种的分布范围。考虑到虚拟物种的不足,提出了未来虚拟物种可能的发展方向(避免过度脱离真实,完善虚拟物种的构成算法,构建虚拟的模式生物、群落及生态系统等)。为帮助研究者快速构建虚拟物种,基于R环境开发了一个虚拟物种构成软件包(SDMvspecies)。虚拟物种可以与真实物种相结合,通过改进模型的构成方法,有利于解决一些真实数据未能解决的问题;虚拟物种的应用也将导致一些新理论的产生,有利于更好地理解生态学原理。     

基于生态位理论研究群落构建的方法进展

理解群落构建过程可以解释生物多样性格局的形成和维持,对于生物多样性保护起到关键作用。生态位理论是群落构建研究的核心框架之一。该理论认为群落构建是生物作用和非生物作用将区域物种库中的物种选入局域群落的确定过程。近年来,随着该领域受到的关注越来越多,研究者不但从物种、谱系或功能等生物多样性维度来研究群落构建,所使用的多样性指数、零模型算法和物种库界定方式等也多种多样。本文回顾了从生物多样性不同维度研究群落构建的优势与局限,总结了群落构建过程中构建零模型和界定物种库时需要注意的一些问题,介绍了部分群落构建研究的最新方法学进展和研究成果。最后,结合近年来的研究案例提出了对未来群落构建研究的一些建议。     

Development of a Finite Element Model of Decompressive Craniectomy

Decompressive craniectomy (DC), an operation whereby part of the skull is removed, is used in the management of patients with brain swelling. While the aim of DC is to reduce intracranial pressure, there is the risk that brain deformation and mechanical strain associated with the operation could damage the brain tissue. The nature and extent of the resulting strain regime is poorly understood at present. Finite element (FE) models of DC can provide insight into this applied strain and hence assist in deciding on the best surgical procedures. However there is uncertainty about how well these models match experimental data, which are difficult to obtain clinically. Hence there is a need to validate any modelling approach outside the clinical setting. This paper develops an axisymmetric FE model of an idealised DC to assess the key features of such an FE model which are needed for an accurate simulation of DC. The FE models are compared with an experimental model using gelatin hydrogel, which has similar poro-viscoelastic material property characteristics to brain tissue. Strain on a central plane of the FE model and the front face of the experimental model, deformation and load relaxation curves are compared between experiment and FE. Results show good agreement between the FE and experimental models, providing confidence in applying the proposed FE modelling approach to DC. Such a model should use material properties appropriate for brain tissue and include a more realistic whole head geometry.     

Predictive modeling of mixed microbial populations in food products: evaluation of two-species models

Predictive microbiology is an emerging research domain in which biological and mathematical knowledge is combined to develop models for the prediction of microbial proliferation in foods. To provide accurate predictions, models must incorporate essential factors controlling microbial growth. Current models often take into account environmental conditions such as temperature, pH and water activity. One factor which has not been included in many models is the influence of a background microflora, which brings along microbial interactions. The present research explores the potential of autonomous continuous-time/two-species models to describe mixed population growth in foods. A set of four basic requirements, which a model should satisfy to be of use for this particular application, is specified. Further, a number of models originating from research fields outside predictive microbiology, but all dealing with interacting species, are evaluated with respect to the formulated model requirements by means of both graphical and analytical techniques. The analysis reveals that of the investigated models, the classical Lotka-Volterra model for two species in competition and several extensions of this model fulfill three of the four requirements. However, none of the models is in agreement with all requirements. Moreover, from the analytical approach, it is clear that the development of a model satisfying all requirements, within a framework of two autonomous differential equations, is not straightforward. Therefore, a novel prototype model structure, extending the Lotka-Volterra model with two differential equations describing two additional state variables, is proposed to describe mixed microbial populations in foods.     

A general class of Bayesian survival models with zero and nonzero cure fractions

We propose a new class of survival models which naturally links a family of proper and improper population survival functions. The models resulting in improper survival functions are often referred to as cure rate models. This class of regression models is formulated through the Box-Cox transformation on the population hazard function and a proper density function. By adding an extra transformation parameter into the cure rate model, we are able to generate models with a zero cure rate, thus leading to a proper population survival function. A graphical illustration of the behavior and the influence of the transformation parameter on the regression model is provided. We consider a Bayesian approach which is motivated by the complexity of the model. Prior specification needs to accommodate parameter constraints due to the non-negativity of the survival function. Moreover, the likelihood function involves a complicated integral on the survival function, which may not have an analytical closed form, and thus makes the implementation of Gibbs sampling more difficult. We propose an efficient Markov chain Monte Carlo computational scheme based on Gaussian quadrature. The proposed method is illustrated with an example involving a melanoma clinical trial.     

The Kermack-McKendrick epidemic model revisited

The Kermack-McKendrick epidemic model of 1927 is an age of infection model, that is, a model in which the infectivity of an individual depends on the time since the individual became infective. A special case, which is formulated as a two-dimensional system of ordinary differential ordinary differential equations, has often been called the Kermack-McKendrick model. One of the products of the SARS epidemic of 2002-2003 was a variety of epidemic models including general contact rates, quarantine, and isolation. These models can be viewed as age of infection epidemic models and analyzed using the approach of the full Kermack-McKendrick model. All these models share the basic properties that there is a threshold between disappearance of the disease and an epidemic outbreak, and that an epidemic will die out without infecting the entire population.     

Context-oriented model validation of individual-based models in ecology: A hierarchically structured approach to validate qualitative,compositional and quantitative characteristics

Validation constitutes a vital process in model development and application, as it ensures the applicability of a model for the intended purposes and trustworthy results within the range of model assumptions. Commonly, independent empirical data sets are statistically compared with the generated model results, which is an adequate approach for models which operate on a single hierarchical level, such as most equation-based models. Individual-based models (IBM) can operate on different organisational levels synchronously and have an inherent complex and variable interaction structure for many applications. Thus a plain comparison of data congruity on the result levels might leave too many questions unanswered. However, a more comprehensive assessment of model validity can require additional investigations which encompass also qualitative and structural relationships.Here we describe a hierarchically structured validation which is oriented towards the investigated context of the model and allows organising the validation process in close relation to the different hierarchical levels which are covered in the model. The context oriented organisation protocol for validation includes the following steps: (1) assessing the different model levels separately, then, (2) applying a set of different techniques such as visual inspection, statistical comparison, involvement of experts, aggregation of data on higher integration levels and experimental validation.The context oriented approach accounts for the specificity of individual-based models – i.e., the dynamic self-organisation of model outcomes from biologically underpinned individual interactions without an inherent determination of properties on higher hierarchical levels – and extends the potential of the validation process qualitatively, as it allows to assess complex structural and causal relations and multi-level feedback processes of the developed models.     

Robust benchmark dose estimation using an infinite family of response functions

Researchers usually estimate benchmark dose (BMD) for dichotomous experimental data using a binomial model with a single response function. Several forms of response function have been proposed to fit dose–response models to estimate the BMD and the corresponding benchmark dose lower bound (BMDL). However, if the assumed response function is not correct, then the estimated BMD and BMDL from the fitted model may not be accurate. To account for model uncertainty, model averaging (MA) methods are proposed to estimate BMD averaging over a model space containing a finite number of standard models. Usual model averaging focuses on a pre-specified list of parametric models leading to pitfalls when none of the models in the list is the correct model. Here, an alternative which augments an initial list of parametric models with an infinite number of additional models having varying response functions has been proposed to estimate BMD for dichotomous response data. In addition, different methods for estimating BMDL based on the family of response functions are derived. The proposed approach is compared with MA in a simulation study and applied to a real dataset. Simulation studies are also conducted to compare the four methods of estimating BMDL.     

Joint modelling of longitudinal measurements and event time data

This paper formulates a class of models for the joint behaviour of a sequence of longitudinal measurements and an associated sequence of event times, including single-event survival data. This class includes and extends a number of specific models which have been proposed recently, and, in the absence of association, reduces to separate models for the measurements and events based, respectively, on a normal linear model with correlated errors and a semi-parametric proportional hazards or intensity model with frailty. Special cases of the model class are discussed in detail and an estimation procedure which allows the two components to be linked through a latent stochastic process is described. Methods are illustrated using results from a clinical trial into the treatment of schizophrenia.     

On optimal associative recall by an incomplete key

A new association scheme is proposed. The fundamental principle of the conventional associative memory models is to solve the matrix equation which is made by the complete (memorized) keys and responses. Therefore, when an incomplete key pattern which is a fraction of memorized key is given to the models as a key, these models can not have an optimal association except for a special case. Analyzing the property of the incomplete key pattern, in this paper, we propose a new association model which behaves optimally for an incomplete key pattern. From the result of the computer simulation, we understand that this model has the expected ability.