Optimal Strategies for Interception,Detection, and Eradication in Plant Biosecurity |
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Authors: | Sara Pasquali Gianni Gilioli Dirk Janssen Stephan Winter |
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Affiliation: | 1. CNR‐IMATI, Milano, Italy;2. DMMT, University of Brescia, Brescia, Italy;3. IFAPA, Centro La Mojonera, Almería, Spain;4. Leibniz Institut DSMZ, Plant Virus Department, Germany |
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Abstract: | The introduction of invasive species causes damages from the economic and ecological point of view. Interception of plant pests and eradication of the established populations are two management options to prevent or limit the risk posed by an invasive species. Management options generate costs related to the interception at the point of entry, and the detection and eradication of established field populations. Risk managers have to decide how to allocate resources between interception, field detection, containment, and eradication minimizing the expected total costs. In this work is considered an optimization problem aiming at determining the optimal allocation of resources to minimize the expected total costs of the introduction of Bemisia tabaci‐transmitted viruses in Europe. The optimization problem takes into account a probabilistic model for the estimation of the percentage of viruliferous insect populations arriving through the trade of commodities, and a population dynamics model describing the process of the vector populations' establishment and spread. The time of field detection of viruliferous insect populations is considered as a random variable. The solution of the optimization problem allows to determine the optimal allocation of the search effort between interception and detection/eradication. The behavior of the search effort as a function of efficacy or search in interception and in detection is then analyzed. The importance of the vector population growth rate and the probability of virus establishment are also considered in the analysis of the optimization problem. |
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Keywords: | Bemisia tabaci invasive species optimization population dynamics probabilistic model |
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