Augmentative biological control is a crop protection method that relies on the repeated introduction of natural enemies to fight agricultural crop pests. The question of the amount, distribution and frequency of natural enemies introductions to best suppress the pests is a central issue. Mathematical results were obtained with hybrid population dynamics models. They indicate that the optimal deployment strategy of natural enemies strongly relies on the presence [4] and sign of density dependence among the natural enemies population [5, 6, 1], and is also affected by the spatial structure of the environment [3]. To evaluate these theoretical predictions in a more realistic, stochastic and spatially explicit setting, a stochastic individual based model has been built on the multi-agent programmable modeling environment Netlogo [7]. Extensive simulatory experiments were performed to assess the effects of density dependent processes as well as spatial structure and stochasticity on augmentative biological control performance and variability. In addition to being used to optimise biological control agents introductions, the model has also been designed to ease the communication with a non-specialist audience regarding the effects of complex population dynamics processes on augmentative biological control efficacy and optimal natural enemies deployment strategies. This objective ressembles that of the Webidemics model in plant epidemics control [2]. References [1] N. Bajeux, F. Grognard, and L. Mailleret. Augmentative biological control when the natural enemies are subject to Allee effects. Journal of Mathematical Biology, Vol 47-1, pp 1561-1587, 2017. [2] N.J. Cunniffe , R.O.J.H. Stutt, R.E. DeSimone, T.R. Gottwald, C.A. Gilligan. Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty. PLoS Computational Biology, 2015. [3] B. Ghosh, F. Grognard and L. Mailleret. Natural enemies deployment in patchy environments for augmentative biological control. Applied Mathematics and Computations, Vol. 266, pp. 982-999, 2015. [4] L. Mailleret and F. Grognard. Global stability and optimisation of a general impulsive biological control model. Mathematical Biosciences, Vol. 221-2, pp. 91-100, 2009. [5] S. Nundloll, L. Mailleret and F. Grognard. Influence of intrapredatory interferences on impulsive biological control efficiency. Bulletin of Mathematical Biology, Vol. 72-8, pp. 2113-2138, 2010. [6] S. Nundloll, L. Mailleret and F. Grognard. Two models of interfering predators in impulsive biological control. Journal of Biological Dynamics, Vol. 4, pp. 102-114, 2010. [7] U. Wilensky, 1999. NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.