Resistance to genetic insect control: Modelling the effects of space

Abstract

Genetic insect control, such as self-limiting RIDL2 (Release of Insects Carrying a Dominant Lethal) technology, is a development of the sterile insect technique which is proposed to suppress wild populations of a number of major agricultural and public health insect pests. This is achieved by mass rearing and releasing male insects that are homozygous for a repressible dominant lethal genetic construct, which causes death in progeny when inherited. The released genetically engineered (‘GE’) insects compete for mates with wild individuals, resulting in population suppression. A previous study modelled the evolution of a hypothetical resistance to the lethal construct using a frequency-dependent population genetic and population dynamic approach. This found that proliferation of resistance is possible but can be diluted by the introgression of susceptible alleles from the released homozygous-susceptible GE males. We develop this approach within a spatial context by modelling the spread of a lethal construct and resistance trait, and the effect on population control, in a two deme metapopulation, with GE release in one deme. Results show that spatial effects can drive an increased or decreased evolution of resistance in both the target and non-target demes, depending on the effectiveness and associated costs of the resistant trait, and on the rate of dispersal. A recurrent theme is the potential for the non-target deme to act as a source of resistant or susceptible alleles for the target deme through dispersal. This can in turn have a major impact on the effectiveness of insect population control.

Population dynamics of engineered underdominance and killer-rescue gene drives in the control of disease vectors

“…Simply, each of the two demes continuously swap a fraction of their population with the other, thus the total amount of migration will depend on the size of each population. Whilst the rates of migration per day considered within this study may appear very low, they actually add up to quite high fractions of the overall populations being exchanged in each generation (comparable to values used in other studies [ 16 , 36 , 37 ]).…”

“…This is used to investigate both the spread of transgenes into the neighbouring population and changes (lasting or transient) in the overall size of a neighbouring population. Two-deme models such as this are well established in the literature for population genetics models of gene drive systems (for example [ 16 , 36 , 37 ]) but these models rarely incorporate population dynamics in addition to genetics. We considered that for gene drive systems predicted to cause significant excess mortality, consideration of population dynamics might be important.…”

Modeling the mutation and reversal of engineered underdominance gene drives

“…(2014)). However, various genetic control measures have been shown to be limited by the potential generation of mutation or resistance (Alphey, Bonsall, Alphey, 2011, Watkinson-Powell, Alphey, 2017, Hammond, Kyrou, Bruttini, North, Galizi, Karlsson, Kranjc, Carpi, D’Aurizio, Crisanti, Nolan, 2017, Unckless, Clark, Messer, 2017, Champer, Reeves, Oh, Liu, Liu, Clark, Messer, 2017b, Champer, Liu, Oh, Reeves, Luthra, Oakes, Clark, Messer, 2017a) the dissociation of gene drive components (e.g. Marshall (2008)) and loss-of function mutation similar to the type studied here (Beaghton et al., 2017).…”

Modeling the mutation and reversal of engineered underdominance gene drives

“…[1,2,19,20]). However, various genetic control measures have been shown to be limited by the potential generation of mutation or resistance [18,[21][22][23][24][25]. A number of studies have also begun to investigate mechanisms capable of reversing gene drives in case they produce unexpected consequences [26][27][28].…”