What can ecosystems learn? Expanding evolutionary ecology with learning theory.

Area of Science:

• Ecology
• Evolutionary Biology
• Computational Neuroscience

Background:

• Ecosystem structure is shaped by species evolution and coevolution.
• Understanding historical influences on ecosystems is key to predicting responses to change.
• Collective ecosystem behaviors lack Darwinian organization, posing a question about alternative organizing principles.

Purpose of the Study:

• To investigate alternative organizing principles for collective ecosystem behaviors.
• To determine if coevolution can create and maintain complex community dynamics.
• To explore how historical conditions shape ecosystem architecture and responses.

Main Methods:

• Incorporated principles from connectionist learning theory.
• Modeled natural selection on ecological interactions as unsupervised learning.
• Analyzed community self-organization and memory formation.

Main Results:

• Ecological communities can self-organize without community-level selection.
• Communities develop a distributed ecological memory of past states.
• Ecosystems exhibit behaviors like state convergence, restoration, and classification based on past experience.
• Identified conditions for hysteresis and catastrophic regime shifts.

Conclusions:

• Connectionist theory offers insights into evolutionary-ecological dynamics.
• Ecological communities can function as connectionist learning systems.
• Communities habituate to past conditions and actively recall them, influencing ecosystem behavior.