This thesis describes the original and significant development of a hierarchical statistical framework in order to realign fine-scale spatial covariate data. An example of the utilisation of this framework is given within the context of biodiversity modelling. Biodiversity is of utmost importance to the correct functioning of ecosystems and the provision of services vital to humanity. Understanding of the impacts on biodiversity by environmental drivers and pressures can help appropriate responses to be taken, to mitigate, halt or reverse damage to habitats. Therefore, linking biodiversity measures with explanatory covariates in statistical models can help understand these relationships and the extent to which certain drivers and pressures are responsible for environmental change. When modelling biodiversity, the scale at which the variables are measured should be considered. Where data are measured at different scales, a situation of misalignment arises. Misaligned data may be subject to measurement error, which can influence the resultant model, if the data are not realigned. In order to realign covariate data, two transformation approaches can be implemented. The first method is to aggregate the response data to the level of the explanatory covariates. The second method is to downscale the covariate data to the response locations. This realignment process is more complex than aggregation of the response, since it requires the uncertainty estimation of the downscaled covariate predictions. The developed framework has possible further applications in fine-scale uncertainty estimation of model covariates, where the scale at which the covariates are given is coarser than that at which the response data are available. Chapter 1 provides an introduction to the main issues and challenges in the thesis: biodiversity, data measurement, modelling techniques, scale and data realignment. The three case studies used in the development of the hierarchical framework are also introduced. Data from Loch Leven on underwater plants are analysed in chapter 2. Carabid data from ten rural locations are considered in chapter 3. In the final case study in chapter 4, coverage abundance data from sites the Countryside Survey across Great Britain are modelled. In chapter 5 the data from chapter 4 are used as the impetus; a hierarchical framework for realigning covariate data is developed and a simulation is created in order to assess its performance relative to the non-realigned model. Chapter 6 provides a summary of the case studies as well as discussion of the main issues and proposals for additional development.