Integrated population modelling of dependent data structures

点击翻译按钮获取中文摘要

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.