Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package

Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, environmental science, epidemiology and social science, and a large suite of modeling tools have been developed for analysing these data. Many utilize conditional autoregressive (CAR) priors to capture the spatial autocorrelation inherent in these data, and software packages such as CARBayes and R-INLA have been developed to make these models easily accessible to others. Such spatial data are typically available for multiple time periods, and the development of methodology for capturing temporally changing spatial dynamics is the focus of much current research. A sizeable proportion of this literature has focused on extending CAR priors to the spatio-temporal domain, and this article presents the R package CARBayesST, which is the first dedicated software package for spatio-temporal areal unit modeling with conditional autoregressive priors. The software package allows to fit a range of models focused on different aspects of spacetime modeling, including estimation of overall space and time trends, and the identification of clusters of areal units that exhibit elevated values. This paper outlines the class of models that the software package implement, before applying them to simulated and two real examples from the fields of epidemiology and housing market analysis

The influence of variations in reedswamp structure and extent upon macroinvertebrates and associated ecological processes within the littoral zone of lakes

Reedswamps are a key feature of the shallows of many lakes, influencing biodiversity and functioning, but are in decline in lakes throughout Europe for reasons that are unclear. Metadata analysis of data extending over 100 years suggested that multiple stressors were implicated in reedbed decline within Windermere (UK), and that the influences of individual stressors should be investigated by comparing genetic diversity and environmental factors across lakes within the same catchment or region. Furthermore, the consequences of changes in reedswamp structure and coverage for whole lake functioning is an important gap in knowledge for Windermere and many other lakes. Macroinvertebrate data from two lakes in the Windermere catchment were used to investigate the influence of reedswamp habitat upon biodiversity, and key ecological processes such as decomposition. A semi-quantitative survey highlighted the importance of reedswamp size, shape, and structure in determining the ways in which macroinvertebrates influence lake functioning. Collection of macroinvertebrates from a wide range of niches along vertical and horizontal axes using a hand-net was a unique approach, and provided novel insights into key ecological processes. For example, seasonal influences were modified by structural heterogeneity, and position within reedswamps. This was supported by the findings of a field-based litter bag experiment; differences in macroinvertebrate seasonal dynamics were associated with differences in litter structure from two species of reed. Furthermore, there were interspecific differences in seasonal patterns of litter deposition. Both macroinvertebrate methods were efficient and effective, and should form the basis of a standardised sampling protocol for the shallows of lakes. This body of research on local variations (~8 m) demonstrates the need for a detailed understanding of how structural heterogeneity influences whole lake functioning. This should include comprehensive food webs that include vertebrates, macroinvertebrates, macrophytes, algae, and microbes for reedswamp and other key habitats