|Nansen, Christian - TEXAS A&M UNI, LUBBOCK|
|Campbell, James - USDA-ARS, MANHATTAN, KS|
|Phillips, Thomas - KANSAS STATE UNIV|
|Subramanyam, Bhadriraju - KANSAS STATE UNIV|
Submitted to: Journal of Economic Entomology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 14, 2008
Publication Date: December 1, 2008
Citation: Nansen, C., Meikle, W.G., Campbell, J., Phillips, T.W., Subramanyam, B. 2008. A binomial and species-independent approach to trap capture analysis of flying insects. Journal of Economic Entomology vol 101,pages 1719-1728. Interpretive Summary: Stored product pests are important because they eat grain and other foods after the crops have been grown and harvested. Stored product pests are often controlled chemically, but this can be risky because that requires spraying stored products, like peanuts and corn, directly. As an alternative, many workers are developing Integrated Pest Management (IPM) programs, in which pest population densities are monitored and action is taken against the pests only when populations get too large. Monitoring is an important part of an IPM program. In stored products, this is often done with traps that catch the pests. The problem is relating the trap capture to a decision of whether or not to do something about the pests, like apply pesticide. In this study data on the trap captures of several species of stored product pests, in several different warehouses with different stored products, were used to develop a sequential sampling plan to help managers make decisions on pest management. The sequential sampling plan requires that the traps be visited one by one, all the insects in each trap are counted, and when all insects pass a certain limit, or falls below a certain limit, the decision is made. To make the plans easier to use, a sampling plan was developed in which only the number of traps with no insects was counted. This second sampling plan was found to be easier to develop and more reliable.
Technical Abstract: Interpretation of trap captures of flying insects is hampered by factors associated with the performance of traps (i.e. lure, trap design, placement) but also by an often poorly defined relationship between trap captures and population density. In this study, we analyzed multiple trapping data sets of various species and demonstrated that the index of aggregation, k, varied considerably with overall trap captures, creating difficulties for the use of a traditional sequential sampling plan. As an alternative, we propose a method of trap capture interpretation which is similar to a sequential sampling plan but is based on proportional dichotomous data (proportion of traps with zero insects captured) and on an assumption of data following a binomial distribution. We showed a consistent non-linear relationship between average trap captures and number of traps with zero captures and that the k can be stabilized by converting trapping data into dichotomous data stabilized. A trap interpretation approach based on number of zero captures is both easier to use, was found to be species-independent, and means that it may be possible to establish meaningful and reliable action thresholds based on trap captures of flying insects. Although developed using trapping data from food facilities, this approach may have application to trapping data from other environments as well.