|Wileyto, E. - UNIV. OF PENNSYLVANIA|
|Norris, James - WAKE-FOREST UNIVERSITY|
|Weaver, D. - MMONTANA STATE UNIVERSITY|
Submitted to: Journal of Insect Behavior
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 24, 2000
Publication Date: N/A
Interpretive Summary: Grain quality, which depends upon the effectiveness of pest management, is important to farm income, consumer health and safety, food costs, and the position of the United States in the world grain market. Historically, management of stored-grain insects has relied heavily on chemical insecticides, but the risk posed to environmental quality and human health makes it necessary to seek safer methods. New management procedures, that would minimize insecticide risk, will require improved monitoring procedures to detect and estimate the level of insect infestation. ARS scientists at the Center for Medical, Agricultural and Veterinary Entomology in Gainesville, Florida, cooperated with university scientists in developing a method for estimating the number of moths in a grain bin or other storage structure. The data used in development of the method were collected by ARS scientists at the former Stored-Product Insects Research and Development Lab in Savannah, GA. The method will be employed mainly by researchers, who will use it to develop improved monitoring systems needed to reduce pesticide risk in stored grain by eliminating the need for routine preventive treatment and guiding the timing of control applications.
Technical Abstract: This paper presents extensions of a method for estimating population size from recapture data obtained by passive self marking and concurrent trapping. The method is based on fitting a continuous-time dynamic model of the trapping and marking process, including several nuisance parameters related to the dynamics of trapping. The model consists of a series of differential equations, and the numerical solution provides a multinomial distribution for captures in the various marking classes. The model is configured by constraining parameter values that are not to be estimated to their default values, and maximum-likelihood provides an unbiased estimate for most configurations of the model. The notable exception occurs when estimating a differential in the operating rates of marking stations and traps (g1); the procedure tends to overestimate the performance of marking stations and population size. Two standard- error intervals usually provide the correct coverage, except where we estimate g1, and the sample is small. Intervals from simple percentiles of a parametric bootstrap improve coverage.