A comparison of abundance estimates from extended batch‐marking and Jolly–Seber‐type experiments

Ecology and Evolution

Laura L. E. Cowen³  Panagiotis Besbeas²  Byron J. T. Morgan¹ 

[1] School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent, U.K;Department of Statistics, Athens University of Economics and Business, Athens, Greece;Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada

关键词: Abundance;    batch mark;    mark–recapture;    open population;   

DOI  :  10.1002/ece3.899

来源: Wiley

【 摘 要 】

Abstract

Little attention has been paid to the use of multi-sample batch-marking studies, as it is generally assumed that an individual's capture history is necessary for fully efficient estimates. However, recently, Huggins et al. (2010) present a pseudo-likelihood for a multi-sample batch-marking study where they used estimating equations to solve for survival and capture probabilities and then derived abundance estimates using a Horvitz–Thompson-type estimator. We have developed and maximized the likelihood for batch-marking studies. We use data simulated from a Jolly–Seber-type study and convert this to what would have been obtained from an extended batch-marking study. We compare our abundance estimates obtained from the Crosbie–Manly–Arnason–Schwarz (CMAS) model with those of the extended batch-marking model to determine the efficiency of collecting and analyzing batch-marking data. We found that estimates of abundance were similar for all three estimators: CMAS, Huggins, and our likelihood. Gains are made when using unique identifiers and employing the CMAS model in terms of precision; however, the likelihood typically had lower mean square error than the pseudo-likelihood method of Huggins et al. (2010). When faced with designing a batch-marking study, researchers can be confident in obtaining unbiased abundance estimators. Furthermore, they can design studies in order to reduce mean square error by manipulating capture probabilities and sample size.

【 授权许可】

CC BY   
© 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.