Integrated population modelling of dependent data structures

在当今全球气候变化的情况下，野生动物种群的建模是至关重要的。本地物种的生存面临着相当大的威胁，有必要确定这些威胁发生的原因，以及可以采取哪些措施来防止物种永远消失。动物种群的数学建模有助于估计重要的人口统计学参数，并可以确认它们与空间、环境和个体协变量的关系。对于简单的数据集，简单的模型是令人满意的。然而，考虑到在大量动物种群中收集的大量详细的个体水平数据，复杂的统计模型的发展严重缺乏。该研究将实现开发个人层面模型的最终目标，该模型可以解释数据集之间的基本相关性。通常情况下，从被研究的单个人群中编译多个数据集。直到最近，对不同类型数据的分析都是以一种零碎的方法进行分析的，从每个数据分析中提取出感兴趣的参数。然而，综合人口建模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对组成数据集的独立性的假设。如果同一个人贡献了多个数据集，则违反了此假设。不正确地将综合总体模型拟合到相关数据集可能导致模型参数的估计有偏差。本研究金提出的研究将提供一个新的个人层面模型，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依赖性。新模型将纳入不完善的个体检测，并提供一种仅使用生活史数据估计可能的亲子关系的方法。还将提供事态发展，以解释提供人口统计和人口统计数据的个人之间的不完全重叠。为了提供一个通用的模型，将推导出新的方法，因此潜在的应用是相当大的。在这项研究中，新的模型将适用于两个长期的案例研究：意大利大天堂国家公园的朗姆酒马鹿岛和高山野山羊。选择这些案例研究是为了评估新建模方法在组成数据集之间具有不同程度重叠的种群中的稳健性，并将有助于回答重要的生物学目标。将讨论模型歧视和拟合优度评估的关键统计方面，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.