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

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.