Markov‐Recapture Population Estimate: A Tool for Improving Interpretation of Trapping Experiments

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Abstract

This paper describes a method of population estimation in which a random, unknown number of individuals is marked using a self—marking bait station (a trap modified for mark and release); animals (both marked and unmarked) may then be captured in an otherwise identical trap, which is available simultaneously. The estimate of the unknown population size is based on the assumption of a closed population and a simple Markov model in which the rates of marking and capture are assumed to be equal. The population size estimator is based on the maximum likelihood technique, and is given by the nest integer less than N = (C + R) 2 /2(R + 1), where R and C are, respectively, the numbers of marked and unmarked individuals found in the trap. The estimator is almost unbiased for a wide range of true population sizes, and over a wide range of times over which the experiment is run, although it becomes negatively biased when the mean number of recaptures is <5. Confidence limits may be obtained using asymptotic maximum likelihood arguments, although relative likelihood methods perform better when the number of recaptures is small.

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