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 since it increases survey effort in areas where the individuals of interest are observed. This work proposes a unit-level model which assumes that counts are related to auxiliary variables, improving the sampling process by assigning different weights to the cells, besides referring to them spatially. The proposed model fits rare and grouped populations arranged on 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 buffalo population in a 24,108 square kilometer area in East Africa. Simulation studies show that the model is efficient in several settings, validating the method proposed in this paper for practical situations.