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Publications (2)5.49 Total impact

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    ABSTRACT: BACKGROUND: Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult due to species biology and behavioural characteristics. The design of robust sampling programmes should be based on an underlying statistical distribution which is sufficiently flexible to capture variations in the spatial distribution of the target species. RESULTS: We compare 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) - to detect insects over a broad range of insect densities. Although the double log and negative binomial models performed well under specific conditions, we show 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 that are 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 of detection probabilities within highly variable systems such as grain storage.
    Pest Management Science 02/2013; · 2.74 Impact Factor
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    David Elmouttie, Andreas Kiermeier, Grant Hamilton
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    ABSTRACT: The presence of insects in stored grain is a significant problem for grain farmers, bulk grain handlers and distributors worldwide. Inspection of bulk grain commodities is essential to detect pests and thereby to reduce the risk of their presence in exported goods. It has been well documented that insect pests cluster in response to factors such as microclimatic conditions within bulk grain. Statistical sampling methodologies for grain, however, have typically considered pests and pathogens to be homogeneously distributed throughout grain commodities. In this paper, a sampling methodology is demonstrated that accounts for the heterogeneous distribution of insects in bulk grain. It is shown that failure to account for the heterogeneous distribution of pests may lead to overestimates of the capacity for a sampling programme to detect insects in bulk grain. The results indicate the importance of the proportion of grain that is infested in addition to the density of pests within the infested grain. It is also demonstrated that the probability of detecting pests in bulk grain increases as the number of subsamples increases, even when the total volume or mass of grain sampled remains constant. This study underlines the importance of considering an appropriate biological model when developing sampling methodologies for insect pests. Accounting for a heterogeneous distribution of pests leads to a considerable improvement in the detection of pests over traditional sampling models.
    Pest Management Science 12/2010; 66(12):1280-6. · 2.74 Impact Factor