Competition in a spatially heterogeneous environment: modelling the risk of spread of a genetically engineered population.
In recent years regulations have been developed to address the risks of releasing genetically engineered organisms into the natural environment. These risks are generally considered to be proportional to the exposure multiplied by the hazard. Exposure is, in part, determined by the spatial spread of the organisms, a component of risk suited to mathematical analysis. In this paper we exampine a mathematical model describing the spread of organisms introduced into a hetereogeneous environment, focusing on the risk of spread and plausibility of containment strategies. Two competing populations are assumed, one the natural species and the other an engineered species or strain, both of which move randomly in a spatially heterogenous environment consisting of alternating favourable and unfavourable patches. The classical Lotka-Volterra competition model with diffusion is used. Analyses of the possible spread and invasion of engineered organisms are thus reduced to finding periodic travelling wave solutions to the model equations. We focus on whether a very small number of engineered organisms can spatially invade a natural population. Initially we investigate the problem for spatially periodic diffusion coefficients and demonstrate that, under the right circumstances and a large enough unfavourable patch, invasion does not succeed. However, if spatially periodic carrying capacities are assumed along with spatially varying diffusion rates, the situation is far more complex. In this case containment of the engineered species is no longer only a simple function of the unfavourable patch length. By using perturbation solutions to the nonuniform steady states, approximate invasion conditions are obtained.
Cruywagen
G C
GC
Sea Fisheries Research Institute, Rogge Bay, Cape Town, , 8012, South Africa.
Kareiva
P
P
Lewis
M A
MA
Murray
J D
JD
D004274:DNA, Recombinant; D004463:Ecology; D005818:Genetic Engineering; D005828:Genetics, Population; D006801:Humans; D007218:Industrial Microbiology; D008954:Models, Biological; D018570:Risk Assessment; D012641:Selection, Genetic; D013029:Space-Time Clustering
D004274:DNA, Recombinant