2016/10/31 by James Wilsenach, Pietro Landi, Cang Hui
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Engineering · Mathematics · Social Sciences · #Adaptation (eye) #Artificial intelligence #Biology #Chaotic #Classical mechanics #Complex system #Computer science #Degrees of freedom (physics and chemistry) #Ecology #Engineering #Epistemology #Evolution and Genetic Dynamics #Evolutionary Game Theory and Cooperation #Evolutionary dynamics #Field (mathematics) #Inertia #Mathematics #Noise (video) #Physics #Plant and animal studies #Population #Range (aeronautics) #Simple (philosophy) #Sociology #Statistical physics #Trait #q-bio.PE
paper · pdf · doi:10.1103/physreve.95.042401
published as Phys. Rev. E 95, 042401 (2017) · 9 pages, 6 figures
openalex publication_date 2017/04/04 · arxiv created 2017/04/06 · arxiv updated 2022/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
One of the properties that make ecological systems so unique is the range of complex behavioural patterns that can be exhibited by even the simplest communities with only a few species. Much of this complexity is commonly attributed to stochastic factors which have very high-degrees of freedom. Orthodox study of the evolution of these simple networks has generally been limited in its ability to explain complexity, since it restricts evolutionary adaptation to an inertia-free process with few degrees of freedom in which only gradual, moderately complex behaviours are possible. We propose a model inspired by particle mediated field phenomena in classical physics in combination with fundamental concepts in adaptation, that suggests that small but high-dimensional chaotic dynamics near to the adaptive trait optimum could help explain complex properties shared by most ecological datasets, such as aperiodicity and pink, fractal noise spectra. By examining a simple predator-prey model and appealing to real ecological data, we show that this type of complexity could be easily confused for or confounded by stochasticity, especially when spurred on or amplified by stochastic factors that share variational and spectral properties with the underlying dynamics.