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

Cowen, Laura L. E., Besbeas, Panagiotis, Morgan, Byron J. T., Schwarz, Carl J. (2014) A comparison of abundance estimates from extended batch-marking and Jolly-Seber type experiments. Ecology and Evolution, 4 (2). pp. 210-218. ISSN 2045-7758. (doi:10.1002/ece3.899) (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) (KAR id:41245)

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.
Official URL:
http://dx.doi.org/10.1002/ece3.899

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 employ-

ing 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.

Item Type: Article
DOI/Identification number: 10.1002/ece3.899
Uncontrolled keywords: Abundance, batch mark, mark–recapture, open population.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QH Natural history > QH541 Ecology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Byron Morgan
Date Deposited: 30 May 2014 17:07 UTC
Last Modified: 17 Aug 2022 10:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41245 (The current URI for this page, for reference purposes)
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