  Models of diffusion driven pattern formation that rely on the Turing
mechanism are utilized in many areas of science. However, many such models
suffer from the defect of requiring fine tuning of parameters or an unrealistic
separation of scales in the diffusivities of the constituents of the system in
order to predict the formation of spatial patterns. In the context of a very
generic model of ecological pattern formation, we show that the inclusion of
intrinsic noise in Turing models leads to the formation of "quasi-patterns"
that form in generic regions of parameter space and are experimentally
distinguishable from standard Turing patterns. The existence of quasi-patterns
removes the need for unphysical fine tuning or separation of scales in the
application of Turing models to real systems.
