野生动物多样性监测图像数据管理系统CameraData介绍

红外相机的应用获得了海量野生动物物种分布和行为的数据信息。然而,如何存贮和管理这些图像数据,并及时提供给研究者、管理决策部门和公众,已成为野生动物监测所面临的新问题。为此,中国科学院动物研究所组织研发了图像数据管理系统CameraData(http://cameradata.ioz.ac.cn)。这是一个开放的网络交互式平台,用于收集和管理通过红外相机所拍摄的野生动物图像数据,集成了野生动物图像数据规范存贮、标准化分析和共享功能。该系统的目标在于促进野生动物图像数据的快速分析和充分利用,为野生动物研究、保护和管理等提供服务。本文对其功能模块、主要构成和使用注意事项等进行了简要介绍。     

GPS项圈系统在野生动物管理与监测中的应用

野生动物管理与监测是生物多样性保护工作的重要内容。而监测手段决定了保护工作中收集数据的质量和数量。收集动物活动位点数据是野生动物管理与监测中的一项重要工作,可通过样线法收集动物活动痕迹(实体)位点或采用无线电遥测获取动物的活动位点等,采用这些位点数据可以研究野生动物种群数量、家域、运动、栖息地利用和选择等。通过给野生动物佩戴GPS项圈,利用GPS项圈系统进行定位记录其活动位点,较前述常规方法更为便利,能高效地收集大量高质量位点,这种方法可以为野生动物的管理与生物多样性保护提供更为可靠的数据基础。     

粪样在野生动物研究中的作用

野生动物数量少,取样难度大;在野外工作时,动物肌肉和血液样品还难以保存。这些给野生动物研究带来很大不便。由于动物粪样容易收集,易于保存,对动物的影响小,在野生动物研究中得到了广泛的应用。粪样分析已经应用在动物的领域、食性、消化动态、疾病与寄生虫、种群数量和遗传结构、有效种群大小、食物链与食物网、能量流与物质流的研究等方面。研究证明,在野生动物的研究和保护中,通过粪样可以得到许多关键性问题的答案。     

常见寿命数据类型及生命表的编制方法

生命表是描述种群死亡过程的有用工具,介绍了4种常见的寿命数据类型;寿终数据,右删失数据,左删失数据和区间型数据特征及其相应的数据分析处理方法即生命表法,乘积限估计和Turbull估计法,对生命表法和乘积限估计法应用上的特点进行了比较,同时还对特殊的寿命数据类型－－截断数据做了简要介绍。     

相机陷阱在野生动物种群生态学中的应用

种群参数估计及空间分布格局是动物生态学和保护生物学领域的重要目标之一.最近十几年来, 相机陷阱(camera trap)作为野外调查的一种非损伤性技术手段,在传统调查方法难以实现的情况下表现出极大优势,被广泛应用于野生动物生态学和保护学研究中.相机陷阱所获取的动物出现数据为野生动物种群提供了极其重要的定量信息.本文从相机陷阱工作原理出发,主要阐述了目前在种群生态学中较为成熟的两类针对具有或不具有天然个体标志物种的模型原理及应用: 1)种群密度和种群数量估计; 2)空间占据率估计.论文特别关注了模型发展的逻辑过程、依赖的假定、使用范围、仍然存在的问题以及未来发展方向.最后, 本文综合分析了相机陷阱在种群参数估计应用中还需注意的问题, 以及其在种群动态和生物多样性研究等方面的发展潜力.     

自然保护区生态旅游对野生动物的影响

目前中国的很多自然保护区开展了生态旅游,但这类活动对野生动物的影响研究却十分薄弱,因此十分有必要在介绍欧美、澳洲学者的研究进展基础上,针对我国的研究现状,提出该领域的研究方向、监测和管理策略.自然保护区的生态旅游活动主要有野生动物观赏、徒步行走、摄影、野外宿营、山地车或雪地车、电动或机动艇游湖、溪涧漂流、环境教育、社区访问等,旅游活动类型、范围、强度、时空分布等是影响对野生动物干扰大小的主要因素.生态旅游对野生动物的影响包括直接影响(个体的行为反应和生理指标改变、繁殖力降低、种群分布和物种组成的改变等)和间接影响(生境破坏、外来种散布和环境污染等).国外生态旅游对鸟类的影响研究较多,主要集中鸟类的惊飞反应、取食、能量消耗、繁殖等方面;对兽类影响的研究主要集中在行为、生理指标改变、种群数量等方面.我国未来的研究应注重收集基础性研究数据和深入探讨一些理论与应用问题,要运用多种技术手段对自然保护区野生动物的行为、生理、种群动态、物种多样性、生境质量、游客的时空分布、态度和行为等进行长期监测,而后将其结果应用到野生动物、生境以及游客的管理决策中去.另外,研究中应重视自然科学和社会科学的多学科交叉融合.     

中国的野生动物红外相机监测需要统一的标准

采用及时、可靠的方法对物种开展有效监测是生物多样性保护的基础。红外相机技术可以获得兽类物种的影像、元数据和分布信息, 是监测生物多样性的有效途径。这项技术在野外便于部署, 规程易于标准化, 可提供野生动物凭证标本(影像)以及物种拍摄位置、拍摄日期与时间、拍摄细节(相机型号等)等附属信息。这些特性使得我们可以积累数以百万计的影像资料和野生动物监测数据。在中国, 红外相机技术已得到广泛应用, 众多机构正在使用红外相机采集并存储野生动物影像以及相关联的元数据。目前, 亟需对红外相机元数据结构进行标准化, 以促进不同机构之间以及与外部保护团体之间的数据共享。迄今全球已建立有数个国际数据共享平台, 例如Wildlife Insights, 但他们离不开与中国的合作, 以有效追踪全球可持续发展的进程。达成这样的合作需要3个基础: 共同的数据标准、数据共享协议和数据禁用政策。我们倡议, 中国保护领域的政府主管部门、机构团体一起合作, 共同制定在国内单位之间以及与国际机构之间共享监测数据的政策、机制与途径。     

应用GIS技术进行野生动物生境研究概况及展望

1　 GIS的特性及对野生动物生境研究产生的影响近年来 ,生物多样性的研究和保护已在多层次、多水平上展开 [2 7] ,其中对野生动物生境的保护和研究是非常重要的一方面。生境 ( habitat)一词首先由美国的 Grinnel于 1 91 7年提出 ,指的是生物的居住场所 ,即生物个体、种群或群落能在其中完成生命过程的空间 [11]。自然界的生物都有它特定的生活环境 ,都有各自要求的适宜的环境条件。由于生物种类繁多 ,生物和环境、生物和生物之间的关系错综复杂 ,因此 ,野生动物生境的保护涉及到许多与时间动态及空间格局相关的问题。传统动物生态学和动物…     

浅议野生动物与人类共患疾病

可能以野生动物为病源的SARS的爆发和流行,使得人们更加关注野生动物传染性疾病对人类健康的威胁。特别是近30年来新出现的多种传染病,自然宿主几乎都是野生动物。本文就野生动物与人类的共患疾病做了简单的历史回顾。新的野生动物传染病的出现提示：如何避免和应对类似事件的再度发生或减少发生的频次、如何处理人与野生动物之间的关系等问题迫在眉睫,而解决这些问题的根本在于人类应该认识自身的位置,尊重自然和其它生命,采取各种有效的措施,保障人类与野生动物安全接触。只有保证生态系统和生物多样性的健康,才能真正维护人类自身的健康。     

生命与健康大数据中心资源

生命与健康多组学数据是生命科学研究和生物医学技术发展的重要基础。然而,我国缺乏生物数据管理和共享平台,不但无法满足国内日益增长的生物医学及相关学科领域的研究发展需求,而且严重制约我国生物大数据整合共享与转化利用。鉴于此,中国科学院北京基因组研究所于2016年初成立生命与健康大数据中心(BIG Data Center, BIGD),围绕国家人口健康和重要战略生物资源,建立生物大数据管理平台和多组学数据资源体系。本文重点介绍BIGD的生命与健康大数据资源系统,主要包括组学原始数据归档库、基因组数据库、基因组变异数据库、基因表达数据库、甲基化数据库、生物信息工具库和生命科学维基知识库,提供生物大数据汇交、整合与共享服务,为促进我国生命科学数据管理、推动国家生物信息中心建设奠定重要基础。     

Case-cohort analysis with accelerated failure time model

Summary .  In a case–cohort design, covariates are assembled only for a subcohort that is randomly selected from the entire cohort and any additional cases outside the subcohort. This design is appealing for large cohort studies of rare disease, especially when the exposures of interest are expensive to ascertain for all the subjects. We propose statistical methods for analyzing the case–cohort data with a semiparametric accelerated failure time model that interprets the covariates effects as to accelerate or decelerate the time to failure. Asymptotic properties of the proposed estimators are developed. The finite sample properties of case–cohort estimator and its relative efficiency to full cohort estimator are assessed via simulation studies. A real example from a study of cardiovascular disease is provided to illustrate the estimating procedure.     

Proportional Hazards Regression for the Analysis of Clustered Survival Data from Case–Cohort Studies

Summary Case–cohort sampling is a commonly used and efficient method for studying large cohorts. Most existing methods of analysis for case–cohort data have concerned the analysis of univariate failure time data. However, clustered failure time data are commonly encountered in public health studies. For example, patients treated at the same center are unlikely to be independent. In this article, we consider methods based on estimating equations for case–cohort designs for clustered failure time data. We assume a marginal hazards model, with a common baseline hazard and common regression coefficient across clusters. The proposed estimators of the regression parameter and cumulative baseline hazard are shown to be consistent and asymptotically normal, and consistent estimators of the asymptotic covariance matrices are derived. The regression parameter estimator is easily computed using any standard Cox regression software that allows for offset terms. The proposed estimators are investigated in simulation studies, and demonstrated empirically to have increased efficiency relative to some existing methods. The proposed methods are applied to a study of mortality among Canadian dialysis patients.     

Regression analysis of doubly censored failure time data using the additive hazards model

Doubly censored failure time data arise when the survival time of interest is the elapsed time between two related events and observations on occurrences of both events could be censored. Regression analysis of doubly censored data has recently attracted considerable attention and for this a few methods have been proposed (Kim et al., 1993, Biometrics 49, 13-22; Sun et al., 1999, Biometrics 55, 909-914; Pan, 2001, Biometrics 57, 1245-1250). However, all of the methods are based on the proportional hazards model and it is well known that the proportional hazards model may not fit failure time data well sometimes. This article investigates regression analysis of such data using the additive hazards model and an estimating equation approach is proposed for inference about regression parameters of interest. The proposed method can be easily implemented and the properties of the proposed estimates of regression parameters are established. The method is applied to a set of doubly censored data from an AIDS cohort study.     

Buckley-James-type estimator with right-censored and length-biased data

We present a natural generalization of the Buckley-James-type estimator for traditional survival data to right-censored length-biased data under the accelerated failure time (AFT) model. Length-biased data are often encountered in prevalent cohort studies and cancer screening trials. Informative right censoring induced by length-biased sampling creates additional challenges in modeling the effects of risk factors on the unbiased failure times for the target population. In this article, we evaluate covariate effects on the failure times of the target population under the AFT model given the observed length-biased data. We construct a Buckley-James-type estimating equation, develop an iterative computing algorithm, and establish the asymptotic properties of the estimators. We assess the finite-sample properties of the proposed estimators against the estimators obtained from the existing methods. Data from a prevalent cohort study of patients with dementia are used to illustrate the proposed methodology.     

Analysis of cohort studies with multivariate and partially observed disease classification data

Complex diseases like cancers can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semiparametric Cox proportional hazards regression model that allows one to examine the heterogeneity in the effect of the covariates by the levels of the different disease traits. For inference in the presence of missing disease traits, we propose a generalization of an estimating equation approach for handling missing cause of failure in competing-risk data. We prove asymptotic unbiasedness of the estimating equation method under a general missing-at-random assumption and propose a novel influence-function-based sandwich variance estimator. The methods are illustrated using simulation studies and a real data application involving the Cancer Prevention Study II nutrition cohort.     

Regression analysis of doubly censored failure time data with applications to AIDS studies

In many epidemiological studies, the survival time of interest is the elapsed time between two related events, the originating event and the failure event, and the times of the occurrences of both events are right or interval censored. We discuss the regression analysis of such studies and a simple estimating equation approach is proposed under the proportional hazards model. The method can easily be implemented and does not involve any iteration among unknown parameters, as full likelihood approaches proposed in the literature do. The asymptotic properties of the proposed regression coefficient estimates are derived and an AIDS cohort study is analyzed to illustrate the proposed approach.     

Haplotype-based association analysis in cohort and nested case-control studies

Genetic epidemiologic studies often collect genotype data at multiple loci within a genomic region of interest from a sample of unrelated individuals. One popular method for analyzing such data is to assess whether haplotypes, i.e., the arrangements of alleles along individual chromosomes, are associated with the disease phenotype or not. For many study subjects, however, the exact haplotype configuration on the pair of homologous chromosomes cannot be derived with certainty from the available locus-specific genotype data (phase ambiguity). In this article, we consider estimating haplotype-specific association parameters in the Cox proportional hazards model, using genotype, environmental exposure, and the disease endpoint data collected from cohort or nested case-control studies. We study alternative Expectation-Maximization algorithms for estimating haplotype frequencies from cohort and nested case-control studies. Based on a hazard function of the disease derived from the observed genotype data, we then propose a semiparametric method for joint estimation of relative-risk parameters and the cumulative baseline hazard function. The method is greatly simplified under a rare disease assumption, for which an asymptotic variance estimator is also proposed. The performance of the proposed estimators is assessed via simulation studies. An application of the proposed method is presented, using data from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study.     

Modeling clustered, discrete, or grouped time survival data with covariates

We have developed methods for modeling discrete or grouped time, right-censored survival data collected from correlated groups or clusters. We assume that the marginal hazard of failure for individual items within a cluster is specified by a linear log odds survival model and the dependence structure is based on a gamma frailty model. The dependence can be modeled as a function of cluster-level covariates. Likelihood equations for estimating the model parameters are provided. Generalized estimating equations for the marginal hazard regression parameters and pseudolikelihood methods for estimating the dependence parameters are also described. Data from two clinical trials are used for illustration purposes.     

Stochastic algorithms for Markov models estimation with intermittent missing data

Multistate Markov models are frequently used to characterize disease processes, but their estimation from longitudinal data is often hampered by complex patterns of incompleteness. Two algorithms for estimating Markov chain models in the case of intermittent missing data in longitudinal studies, a stochastic EM algorithm and the Gibbs sampler, are described. The first can be viewed as a random perturbation of the EM algorithm and is appropriate when the M step is straightforward but the E step is computationally burdensome. It leads to a good approximation of the maximum likelihood estimates. The Gibbs sampler is used for a full Bayesian inference. The performances of the two algorithms are illustrated on two simulated data sets. A motivating example concerned with the modelling of the evolution of parasitemia by Plasmodium falciparum (malaria) in a cohort of 105 young children in Cameroon is described and briefly analyzed.     

Normal frailty probit model for clustered interval‐censored failure time data

Clustered interval‐censored data commonly arise in many studies of biomedical research where the failure time of interest is subject to interval‐censoring and subjects are correlated for being in the same cluster. A new semiparametric frailty probit regression model is proposed to study covariate effects on the failure time by accounting for the intracluster dependence. Under the proposed normal frailty probit model, the marginal distribution of the failure time is a semiparametric probit model, the regression parameters can be interpreted as both the conditional covariate effects given frailty and the marginal covariate effects up to a multiplicative constant, and the intracluster association can be summarized by two nonparametric measures in simple and explicit form. A fully Bayesian estimation approach is developed based on the use of monotone splines for the unknown nondecreasing function and a data augmentation using normal latent variables. The proposed Gibbs sampler is straightforward to implement since all unknowns have standard form in their full conditional distributions. The proposed method performs very well in estimating the regression parameters as well as the intracluster association, and the method is robust to frailty distribution misspecifications as shown in our simulation studies. Two real‐life data sets are analyzed for illustration.