Two-stage sequential Bayesian study design for species estimation

Guillera-Arroita, Gurutzeta and Ridout, Martin S. and Morgan, Byron J. T. (2014) Two-stage sequential Bayesian study design for species estimation. Journal of Agricultural, Biological, and Environmental Statistics, 19 (2). pp. 278-291. ISSN 1085-7117. (doi:https://doi.org/10.1007/s13253-014-0171-4) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

Abstract

A problem of interest for ecology and conservation is that of determining the best al- location of survey effort in studies aimed at estimating the proportion of sites occupied by a species. Many species are difficult to detect and often remain undetected during surveys at sites where they are present. Hence, for the estimator of species occupancy to be unbiased, detectability needs to be taken into account. In such studies there is a trade-off between sampling more sites and expending more survey effort within each site. This design problem has not been addressed to date with an explicit consideration of the uncertainty in assumed parameter values. In this article we apply sequential and Bayesian design techniques and show how a simple two-stage design can significantly improve the efficiency of the study. We further investigate the optimal allocation of survey effort between the two study stages, given a prior distribution for the parame- ter values. We address this problem using asymptotic approximations and then explore how the results change when the sample size is small, considering second-order approx- imations and highlighting the value of simulations as a tool for study design. Given the efficiency gain, we recommend following the sequential design approach for species occupancy estimation. This article has supplementary material online.

Item Type:	Article
Uncontrolled keywords:	Binary data; Imperfect detection; Multistage; Optimal design; Pilot study; Second-order approximation.
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics Q Science > QH Natural history > QH541 Ecology
Divisions:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User:	Byron Morgan
Date Deposited:	30 May 2014 13:05 UTC
Last Modified:	02 Jun 2014 11:22 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/41242 (The current URI for this page, for reference purposes)