DriverSEAT: A spatially-explicit stochastic modelling framework for the evaluation of gene drives in novel target species

Abstract

Gene drives represent a potentially ground breaking technology for the control of undesirable species or the introduction of desirable traits in wild population, and there is strong interest in applying these technologies to a wide range of species across many domains including agriculture, health, conservation and biosecurity. There remains however considerable uncertainty regarding the feasibility and efficacy of gene drives in various species, based in particular on biological and ecological specificities of each target. In this paper we introduce DriverSEAT, a new spatial, modular modelling framework designed to assess the outcome of gene drives in a range of target species based on their specific ecological dynamics and genetics. In addition to the main structure and characteristics of the model, we present an example of its application on scenarios of genetic control of weeds, a potential candidate for gene drive control that presents significant challenges associated with plant population dynamics. We illustrate here how the results from DriverSEAT can inform on the potential value of gene drives in this specific context, and generally provide ecologically informed guidance for the development and feasibility of gene drives as a control method in new target species.