One of the properties that make ecological systems so unique is the range ofcomplex behavioural patterns that can be exhibited by even the simplestcommunities with only a few species. Much of this complexity is commonlyattributed to stochastic factors which have very high-degrees of freedom.Orthodox study of the evolution of these simple networks has generally beenlimited in its ability to explain complexity, since it restricts evolutionaryadaptation to an inertia-free process with few degrees of freedom in which onlygradual, moderately complex behaviours are possible. We propose a modelinspired by particle mediated field phenomena in classical physics incombination with fundamental concepts in adaptation, that suggests that smallbut high-dimensional chaotic dynamics near to the adaptive trait optimum couldhelp explain complex properties shared by most ecological datasets, such asaperiodicity and pink, fractal noise spectra. By examining a simplepredator-prey model and appealing to real ecological data, we show that thistype of complexity could be easily confused for or confounded by stochasticity,especially when spurred on or amplified by stochastic factors that sharevariational and spectral properties with the underlying dynamics.
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