Pest Management Science (2013) 69, 1073-1079

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David Elmouttie, Paul Flinn, Andreas Kiermeier, Bhadriraju Subramanyam, David Hagstrum and Grant Hamilton (2013)
Sampling stored-product insect pests: a comparison of four statistical sampling models for probability of pest detection
Pest Management Science 69 (9), 1073-1079
Abstract:
Background
Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult owing to species biology and behavioural characteristics. The design of robust sampling programmes should be based on an underlying statistical distribution that is sufficiently flexible to capture variations in the spatial distribution of the target species.
Results
Comparisons are made of the accuracy of four probability-of-detection sampling models - the negative binomial model,¹ the Poisson model,¹ the double logarithmic model² and the compound model³ - for detection of insects over a broad range of insect densities. Although the double log and negative binomial models performed well under specific conditions, it is shown that, of the four models examined, the compound model performed the best over a broad range of insect spatial distributions and densities. In particular, this model predicted well the number of samples required when insect density was high and clumped within experimental storages.
Conclusions
This paper reinforces the need for effective sampling programs designed to detect insects over a broad range of spatial distributions. The compound model is robust over a broad range of insect densities and leads to substantial improvement in detection probabilities within highly variable systems such as grain storage.
(The abstract is excluded from the Creative Commons licence and has been copied with permission by the publisher.)
Link to article at publishers website

Database assignments for author(s): Bhadriraju Subramanyam, Paul W. Flinn

Research topic(s) for pests/diseases/weeds:
surveys/sampling/distribution

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Pest and/or beneficial records:

Beneficial	Pest/Disease/Weed	Crop/Product	Country	Quarant.