Model-based inference for rare and clustered populations from adaptive cluster sampling using auxiliary variables

Abstract

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, as it increases survey effort in areas where individuals of interest are observed. This work aims to propose a unit-level model that assumes counts are related to auxiliary variables, improving the sampling process by assigning different weights to the cells and accounting for spatial structure. The proposed model fits rare and clustered populations distributed over a regular grid within 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 buffalo population in a 24,108 km² area of East Africa. Simulation studies show that the model is efficient under several settings, validating the methodology proposed in this dissertation for practical applications.