Journal article
An extravariation model for improving confidence intervals of population size estimates from removal data
Canadian Journal of Fisheries and Aquatic Sciences, Vol.53(11), pp.2533-2539
1996
Abstract
We propose a new model for estimating the size of a population from successive catches taken during a removal experiment. The data from these experiments often have excessive variation, known as overdispersion, as compared with that predicted by the multinomial model. The new model allows catchability to vary randomly among samplings, which accounts for overdispersion. When the catchability is assumed to have a beta distribution, the likelihood function, which is refered to as beta-multinomial, is derived, and hence the maximum likelihood estimates can be evaluated. Simulations show that in the presence of extravariation in the data, the confidence intervals have been substantially underestimated in previous models (Leslie-DeLury, Moran) and that the new model provides more reliable confidence intervals. The performance of these methods was also demonstrated using two real data sets: one with overdispersion, from smallmouth bass (Micropterus dolomieu), and the other without overdispersion, from rat (Rattus rattus).
Details
- Title
- An extravariation model for improving confidence intervals of population size estimates from removal data
- Authors/Creators
- Y-G Wang (Author/Creator)N.R. Loneragan (Author/Creator)
- Publication Details
- Canadian Journal of Fisheries and Aquatic Sciences, Vol.53(11), pp.2533-2539
- Publisher
- National Research Council of Canada
- Identifiers
- 991005542211807891
- Copyright
- © 1996 NRC.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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