University of Bath

About: University of Bath is a education organization based out in Bath, Bath and North East Somerset, United Kingdom. It is known for research contribution in the topics: Population & Photonic-crystal fiber. The organization has 15830 authors who have published 39608 publications receiving 1358769 citations. The organization is also known as: Bath University.

An intuitive Bayesian spatial model for disease mapping that accounts for scaling

Abstract: In recent years, disease mapping studies have become a routine application within geographical epidemiology and are typically analysed within a Bayesian hierarchical model formulation. A variety of model formulations for the latent level have been proposed but all come with inherent issues. In the classical BYM (Besag, York and Mollie) model, the spatially structured component cannot be seen independently from the unstructured component. This makes prior definitions for the hyperparameters of the two random effects challenging. There are alternative model formulations that address this confounding; however, the issue on how to choose interpretable hyperpriors is still unsolved. Here, we discuss a recently proposed parameterisation of the BYM model that leads to improved parameter control as the hyperparameters can be seen independently from each other. Furthermore, the need for a scaled spatial component is addressed, which facilitates assignment of interpretable hyperpriors and make these transferable between spatial applications with different graph structures. The hyperparameters themselves are used to define flexible extensions of simple base models. Consequently, penalised complexity priors for these parameters can be derived based on the information-theoretic distance from the flexible model to the base model, giving priors with clear interpretation. We provide implementation details for the new model formulation which preserve sparsity properties, and we investigate systematically the model performance and compare it to existing parameterisations. Through a simulation study, we show that the new model performs well, both showing good learning abilities and good shrinkage behaviour. In terms of model choice criteria, the proposed model performs at least equally well as existing parameterisations, but only the new formulation offers parameters that are interpretable and hyperpriors that have a clear meaning.

Global biogeography and ecology of body size in birds

Abstract: In 1847, Karl Bergmann proposed that temperature gradients are the key to understanding geographic variation in the body sizes of warm-blooded animals. Yet both the geographic patterns of body-size variation and their underlying mechanisms remain controversial. Here, we conduct the first assemblage-level global examination of 'Bergmann's rule' within an entire animal class. We generate global maps of avian body size and demonstrate a general pattern of larger body sizes at high latitudes, conforming to Bergmann's rule. We also show, however, that median body size within assemblages is systematically large on islands and small in species-rich areas. Similarly, while spatial models show that temperature is the single strongest environmental correlate of body size, there are secondary correlations with resource availability and a strong pattern of decreasing body size with increasing species richness. Finally, our results suggest that geographic patterns of body size are caused both by adaptation within lineages, as invoked by Bergmann, and by taxonomic turnover among lineages. Taken together, these results indicate that while Bergmann's prediction based on physiological scaling is remarkably accurate, it is far from the full picture. Global patterns of body size in avian assemblages are driven by interactions between the physiological demands of the environment, resource availability, species richness and taxonomic turnover among lineages.