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Dernière mise à jour : Mai 2018

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Institut Sophia Agrobiotech

UMR INRA - Univ. Nice Sophia Antipolis - Cnrs

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Biological Control

17 May 2018

Biological Control
© 2018 Elsevier B.V.
Improving the efficiency of augmentative biological control with arthropod natural enemies: a modeling approach

Abstract

A better understanding of the life-history traits of biocontrol agents and their effect on population dynamics is key to obtaining more efficient pest control and generating higher economic returns for biocontrol practitioners. To this end, we constructed an optimality simulation model based on principles of the behavioral ecology of natural enemies. This model allows for the identification of the most important life-history traits of natural enemies (e.g., fecundity, longevity, attack rate, competition and dispersal), taking into account the costs and benefits for biocontrol practitioners. The model was kept general and was designed in such a way that it can be adapted to different target species and their specific ecology (natural enemy-pest-plant combination). Results indicate strong interactions between the optimized life-history traits of the biocontrol agents. Two different optimized life-history strategies for the agents were found with higher potential economic returns. These strategies differ most significantly in the plant-leaving decision and host handling time of the biocontrol agent, but also in their respective fecundity, longevity and dispersal ability. The preferred strategy depends on the number of agents released and the growth rate of the plant. Information from these optimality models can help to determine which agents should be released and how they should be released in a specific agro-ecological situation.

Keywords

  • Natural enemies; 
  • Behavioral ecology; 
  • Life history traits; 
  • Individual-based model; 
  • Genetic algorithm; 
  • Cost efficiency

Plouvier, W.N., and Wajnberg, E. Improving the efficiency of augmentative biological control with arthropod natural enemies: a modeling approach. Biological Control. DOI: 10.1016/j.biocontrol.2018.05.010

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