  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. This work aims to propose a unit-level model which
assumes that counts are related to auxiliary variables, improving the sampling
process, assigning different weights to the cells, besides referring them
spatially. The proposed model fits rare and grouped populations, disposed over
a regular grid, in a Bayesian framework. The approach is compared to
alternative methods using simulated data and a real experiment in which
adaptive samples were drawn from an African Buffaloes population in a 24,108
square kilometers area of East Africa. Simulation studies show that the model
is efficient under several settings, validating the methodology proposed in
this paper for practical situations.
