Abstract
This paper reviews recent development of a method for estimating insect populations. It is like mark-recapture methods, except that marking is done passively at bait stations by the insects themselves, and capture probabilities are generated using a simple Markov process model. Assumptions about rates of marking and capture are made from the sampling scheme, and the estimate is based upon the resulting multinomial probability distribution and maximum likelihood methods. The paper continues to review the sampling distributions for the population estimate, revealed by simulation, and explores correction of the bias. Relative likelihood based confidence intervals are compared with two standard error intervals, and found to perform better over a wide range of parameter values, especially where the number of recaptures is small. The method tends to become biased when used in an open or growing population. Goodness of fit tests are possible with the added degrees of freedom, but are not very powerful.
Keywords
Trap; population; estimate; markov process; mark-recapture; likelihood, relative likelihood interval, profile likelihood interval
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Wileyto, E. Paul
(1994).
"MARKOV-RECAPTURE POPULATION ESTIMATES,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1355
MARKOV-RECAPTURE POPULATION ESTIMATES
This paper reviews recent development of a method for estimating insect populations. It is like mark-recapture methods, except that marking is done passively at bait stations by the insects themselves, and capture probabilities are generated using a simple Markov process model. Assumptions about rates of marking and capture are made from the sampling scheme, and the estimate is based upon the resulting multinomial probability distribution and maximum likelihood methods. The paper continues to review the sampling distributions for the population estimate, revealed by simulation, and explores correction of the bias. Relative likelihood based confidence intervals are compared with two standard error intervals, and found to perform better over a wide range of parameter values, especially where the number of recaptures is small. The method tends to become biased when used in an open or growing population. Goodness of fit tests are possible with the added degrees of freedom, but are not very powerful.
