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 model(2) and the compound model(3) - 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.
Keywords: Poisson; compound model; double logarithmic model; grains; heterogeneity; negative binomial.
© 2013 Society of Chemical Industry.