What can ecosystems learn? Expanding evolutionary ecology with learning theory

TL;DR

This paper explores how ecological communities can self-organize and adapt through principles of connectionist learning, providing a new perspective on community-level behavior without relying on Darwinian selection.

Contribution

It introduces a novel framework linking ecological coevolution with connectionist learning theories, showing communities can self-organize similarly to neural networks.

Findings

Communities can self-organize without community-level selection.

Ecological interactions can be viewed as unsupervised learning processes.

Communities habituate and recall environmental conditions through internal organization.

Abstract

Understanding how the structure of community interactions is modified by coevolution is vital for understanding system responses to change at all scales. However, in absence of a group selection process, collective community behaviours cannot be organised or adapted in a Darwinian sense. An open question thus persists: are there alternative organising principles that enable us to understand how coevolution of component species creates complex collective behaviours exhibited at the community level? We address this issue using principles from connectionist learning, a discipline with well-developed theories of emergent behaviours in simple networks. We identify conditions where selection on ecological interactions is equivalent to 'unsupervised learning' (a simple type of connectionist learning) and observe that this enables communities to self organize without community-level selection. Despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal organisation that habituates to past environmental conditions and actively recalling those conditions.