Pooled population parameter from mark-recapture data. 1994

J W Hargrove, and C H Borland
Department of Veterinary Services, ODA Insect Pest Management Initiative, Causeway, Zimbabwe.

The reduced capture history (RCH), compiled from complete capture histories of uniquely marked animals, for a given pooling interval contains the same information as would be obtained from experiments where (i) a single sample lasts the duration of the pooling interval; (ii) an identical batch mark is applied to animals captured in a series of samples carried out during the pooling interval. For stationary populations, biases are calculated for the RCH estimates for all parameters in the Jolly-Seber (J-S) model. The results are verified using simulation. The biases are functions of the survival and capture probabilities and the degree of pooling; they are less than 5% for the total population, birth and survival rates, and probability of capture during the pooling interval if the mortality and capture probabilities do not exceed about 50% per pooling interval. The marked population, marked fraction, and probability of recapture cannot estimated directly by the RCH method but can be obtained iteratively from the bias formulae. The biases in other parameters can be reduced by the same procedure. Alternative estimates are derived that are not detectably biased, for any estimate, for mortality and capture probability up to about 60% per pooling period. The new estimates have higher sample variances than the RCH estimates, but for large populations with high mortalities and capture probabilities the difference is small.

UI MeSH Term Description Entries
D009010 Monte Carlo Method In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993) Method, Monte Carlo
D011153 Population The total number of individuals inhabiting a particular region or area. School Age Population,School-Age Population,Population, School Age,Population, School-Age,Populations,Populations, School Age,Populations, School-Age,School Age Populations,School-Age Populations
D011336 Probability The study of chance processes or the relative frequency characterizing a chance process. Probabilities
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D000835 Animals, Wild Animals considered to be wild or feral or not adapted for domestic use. It does not include wild animals in zoos for which ANIMALS, ZOO is available. Animals, Nondomestic,Animals, Nondomesticated,Animals, Feral,Stray Animals,Animal, Feral,Animal, Nondomestic,Animal, Nondomesticated,Animal, Stray,Animal, Wild,Animals, Stray,Feral Animal,Feral Animals,Nondomestic Animal,Nondomestic Animals,Nondomesticated Animal,Nondomesticated Animals,Stray Animal,Wild Animal,Wild Animals
D001699 Biometry The use of statistical and mathematical methods to analyze biological observations and phenomena. Biometric Analysis,Biometrics,Analyses, Biometric,Analysis, Biometric,Biometric Analyses
D013534 Survival Continuance of life or existence especially under adverse conditions; includes methods and philosophy of survival.
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model

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