Model-based Inference for Rare and Clustered Populations from Adaptive Cluster Sampling using Auxiliary Variables

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 individua...

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Bibliographic Details
Published in:arXiv.org 2020-03
Main Authors: Izabel Nolau de Souza, Mota Gonçalves, Kelly Cristina, João Batista de Morais Pereira
Format: Article
Language:English
Subjects:
Adaptive sampling
Buffalo
Clusters
Computer simulation
Endangered species
Populations
Online Access:Get full text
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Summary: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.
ISSN:2331-8422