  System-environment interactions are intrinsically nonlinear and dependent on
the interplay between many degrees of freedom. The complexity may be even more
pronounced when one aims to describe biologically motivated systems. In that
case, it is useful to resort to simplified models relying on effective
stochastic equations. A natural consideration is to assume that there is a
noisy contribution from the environment, such that the parameters which
characterize it are not constant but instead fluctuate around their
characteristic values. From this perspective, we propose a stochastic
generalization of the nonlocal Fisher-KPP equation where, as a first step,
environmental fluctuations are Gaussian white noises, both in space and time.
We apply analytical and numerical techniques to study how noise affects
stability and pattern formation in this context. Particularly, we investigate
noise induced coherence by means of the complementary information provided by
the dispersion relation and the structure function.
