  Rare populations, such as endangered species, drug users and individuals
infected by rare diseases, tend to cluster in regions. Adaptive cluster designs
are generally applied to obtain information from clustered and sparse
populations. The aim of this work is to propose a unit-level mixture model for
clustered and sparse populations when the data are obtained from an adaptive
cluster sample. Our approach considers heterogeneity among units belonging to
different clusters. The proposed model is evaluated using simulated data and a
real experiment in which adaptive samples were drawn from an enumeration of a
waterfowl species in a 5,000 km2 area of central Florida.
