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Integrated population modelling of dependent data structures

依赖数据结构的集成总体建模

基本信息

批准号：
NE/J018473/1
负责人：
Rachel Sara McCrea
金额：
$ 29.52万
依托单位：
University of Kent
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FJ018473%2F1
关键词：
Integrated population modelling dependent data

项目摘要

The modelling of wild animal populations is of utmost importance in today's climate of global change. There is considerable threat to the survival of native species and it is necessary to determine why these threats are occurring and what can be done to prevent the loss of species forever. The mathematical modelling of animal populations facilitates the estimation of important demographic parameters and can confirm their relationship with spatial, environmental and individual covariates.Simple models were satisfactory for simple data sets. However, the development of sophisticated statistical models is severely lacking given the wealth of detailed individual level data being collected on a huge range of animal populations. This fellowship will achieve the ultimate goal of developing an individual level model which accounts for fundamental correlations between data sets.It is often the case that multiple data sets are compiled from a single population under study. Until recently analyses on the different types of data were analysed in a piecemeal approach, extracting the parameters of interest from each data analysis. However the theory of integrated population modelling demonstrated the benefits of modelling multiple types of data within one coherent framework. The theory of integrated population modelling relies on assumptions of independence of the component data sets. This assumption is violated if the same individuals contribute to more than one data set. Incorrectly fitting integrated population models to dependent data sets can result in biased estimates of model parameters.The research proposed within this fellowship will provide a new individual level model which will include all available information and will correctly account for the dependence of the different data types. The new model will incorporate imperfect detection of individuals and offer an approach to estimate likely parentage using just life history data. Developments will also be offered to account for incomplete overlap between individuals contributing to demographic and population count data. The new methodology will be derived in order to provide an all-purpose model and as such the potential applications are considerable. Within this fellowship the new models will be fitted to two long-running case studies: Isle of Rum red deer and Alpine ibex in the Gran Paradiso National Park, Italy. These case studies have been selected to allow the robustness of the new modelling approaches to be assessed for populations with varying degrees of overlap between component data sets and will facilitate the answering of important biological objectives. Key statistical aspects of model discrimination and goodness-of-fit assessment will be addressed and software promoting the use of the new procedures will be released.

野生动物种群的建模在当今全球气候变化中至关重要。当地物种的生存受到相当大的威胁，有必要确定为什么这些威胁正在发生，以及可以做些什么来防止物种永远消失。动物种群的数学模型有助于估计重要的人口统计参数，并能确定它们与空间、环境和个体协变量的关系。然而，由于收集了大量动物种群的详细个体水平数据，因此严重缺乏复杂统计模型的开发。该研究金将实现开发个人水平模型的最终目标，该模型将解释数据集之间的基本相关性。通常情况下，多个数据集是从所研究的单个人口中编制的。直到最近，对不同类型数据的分析都是以零敲碎打的方式进行的，从每次数据分析中提取有关参数。然而，综合人口建模理论表明，在一个连贯一致的框架内对多种类型的数据建模是有好处的。综合总体建模理论依赖于各组成数据集独立性的假设。如果同一个人对多个数据集做出贡献，则违反了这一假设。不正确地将综合总体模型拟合到相关数据集可能会导致模型参数的有偏估计。本奖学金中提出的研究将提供一个新的个体水平模型，该模型将包括所有可用的信息，并将正确地解释不同数据类型的相关性。新模型将结合对个体的不完美检测，并提供一种仅使用生活史数据来估计可能的亲子关系的方法。还将提供发展情况，以说明为人口统计和人口计数数据作出贡献的个人之间的不完全重叠。新的方法将派生，以提供一个通用的模型，因此，潜在的应用是相当大的。在这个奖学金的新模型将适合两个长期运行的案例研究：朗姆酒岛红鹿和阿尔卑斯山北山羊在大天堂国家公园，意大利。选择这些案例研究是为了评估新的建模方法对各组成数据集之间有不同程度重叠的人群的稳健性，并将有助于回答重要的生物学目标。将处理模型判别和拟合优度评估的关键统计问题，并将发布促进使用新程序的软件。