Analysis and prediction of agricultural pest dynamics with Tiko'n, a generic tool to develop agroecological food web models

While several well-validated crop growth models are currently widely used, very few crop pest models of the same caliber have been developed or applied, and pest models that take trophic interactions into account are even rarer. This may be due to several factors, including 1) the difficulty of representing complex agroecological food webs in a quantifiable model, and 2) the general belief that pesticides effectively remove insect pests from immediate concern. However, pests currently claim a substantial amount of harvests every year (and account for additional control costs), and the impact of insects and of their trophic interactions on agricultural crops cannot be ignored, especially in the context of changing climates and increasing pressures on crops across the globe. Unfortunately, most integrated pest management frameworks rely on very simple models (if at all), and most examples of successful agroecological management remain more anecdotal than scientifically replicable. In light of this, there is a need for validated and robust agroecological food web models that allow users to predict the response of these webs to changes in management, crops or climate, both in order to predict future pest problems under a changing climate as well as to develop effective integrated management plans. Here we present Tiko'n, a Python-based software whose API allows users to rapidly build and validate trophic web agroecological models that predict pest dynamics in the field. The programme uses a Bayesian inference approach to calibrate the models according to field data, allowing for the reuse of literature data from various sources and reducing the need for extensive field data collection. We apply the model to the cononut black-headed caterpillar (Opisina arenosella) and associated parasitoid data from Sri Lanka, showing how the modeling framework can be used to rapidly develop, calibrate and validate models that elucidate how the internal structures of food webs determine their behaviour and allow users to evaluate different integrated management options.