Integrated population modelling of dependent data structures

在当今全球变化的气候中，野生动物种群的建模至关重要。本土物种的生存受到相当大的威胁，有必要确定为什么会出现这些威胁，以及可以做些什么来防止物种永远丧失。动物种群的数学模型有助于估计重要的人口统计学参数，并能确定它们与空间、环境和个体协变量的关系。然而，由于收集了大量关于动物种群的详细的个体水平数据，因此严重缺乏复杂的统计模型的开发。这种研究金将实现开发个人水平模型的最终目标，该模型解释了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群体汇编而来的。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，从每个数据分析中提取感兴趣的参数。然而，综合人口模型理论证明了在一个连贯的框架内对多种类型的数据进行建模的好处。综合人口模型的理论依赖于各组成部分数据集的独立性假设。如果相同的人贡献了一个以上的数据集，则违反了这一假设。不正确地将整合的总体模型与相依的数据集相匹配会导致模型参数的偏向估计。在该联谊会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信息，并将正确地解释不同数据类型的相依性。新的模型将包含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，以说明提供人口和人口统计数据的个人之间不完全重叠的情况。将推出新的方法，以便提供一种通用模式，因此，潜在的应用是相当可观的。在这一奖学金范围内，新模型将适用于两个长期运行的案例研究：意大利Gran Paradiso国家公园的朗姆红鹿岛和阿尔卑斯山ibex。选择这些案例研究是为了评估新的建模方法的稳健性，以评估各组成部分数据集之间重叠程度不同的群体，并将有助于回答重要的生物学目标。将讨论模式歧视和拟合优度评估的主要统计方面，并将发布促进使用新程序的软件。

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.