Design and analysis of clustered, unmatched resource selection studies

Studies which measure animals’ positions over time are a vital tool in understanding the process of resource selection by animals. By comparing a sample of locations that are used by animals with a sample of available points, the types of locations that are preferred by animals can be analysed by using logistic regression. Random-effects logistic regression has been proposed to deal with the repeated measurements that are observed for each animal, but we find that this is not feasible in studies where the sample of available points cannot readily be matched to specific animals. Instead, we investigate the use of marginal logistic models with robust variance estimators, by using a study of Australian bush rats as a case-study. Simulation is used to check the properties of the approach and to explore alternative designs.