Evolutionary Fields Can Explain Patterns of High Dimensional Complexity in Ecology

TL;DR

This paper introduces a physics-inspired model suggesting that high-dimensional chaotic dynamics near adaptive optima can explain complex ecological patterns like aperiodicity and fractal noise, challenging the stochasticity explanation.

Contribution

It proposes a novel model combining particle-mediated fields and adaptation concepts to explain ecological complexity beyond traditional stochastic explanations.

Findings

Chaotic dynamics near adaptive optima can produce complex ecological patterns.

Complexity can be confounded with stochasticity due to similar spectral properties.

The model explains observed ecological behaviors using high-dimensional chaos.

Abstract

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.