Model-based Inference for Rare and Clustered Populations

Rare populations, such as endangered animals and plants, drug users and individuals with rare diseases, tend to cluster in regions. Adaptive cluster sampling is generally applied to obtain information from clustered and sparse populations since it increases survey effort in areas where the individuals of interest are observed. In this work we revisit some model-based approaches under the Bayesian framework to make inference about the number of individuals in a sparse and clustered population. The approaches allow us to use knowledge of the population to inform both the sampling design and inference, resulting in improved population estimates when compared to the design-based estimates.