Decoding species coexistence: A reinforcement learning perspective

TL;DR

This paper introduces a reinforcement learning framework to model species coexistence in ecology, demonstrating that adaptive mobility can promote stable biodiversity contrary to fixed-mobility models.

Contribution

It presents a novel Q-learning based spatial RPS model where adaptive mobility enables stable coexistence, addressing discrepancies with empirical observations.

Findings

Species coexistence remains stable across a broad range of migration rates.

Individuals develop behavioral tendencies balancing survival and predation.

Adaptive mobility confers an evolutionary advantage over fixed mobility.

Abstract

A central goal in ecology is to understand how biodiversity is maintained. Previous theoretical works have employed the rock-paper-scissors (RPS) game as a toy model, demonstrating that population mobility is crucial in determining the species' coexistence. One key prediction is that biodiversity is jeopardized and eventually lost when mobility exceeds a certain value--a conclusion at odds with empirical observations of highly mobile species coexisting in nature. To address this discrepancy, we introduce a reinforcement learning framework and study a spatial RPS model, where individual mobility is adaptively regulated via a Q-learning algorithm rather than held fixed. Our results show that all three species can coexist stably, with extinction probabilities remaining low across a broad range of baseline migration rates. Mechanistic analysis reveals that individuals develop two behavioral tendencies: survival priority (escaping from predators) and predation priority (remaining near prey). While species coexistence emerges from the balance of the two tendencies, their imbalance jeopardizes biodiversity. Notably, there is a symmetry-breaking of action preference in a particular state that is responsible for the divergent species densities. Furthermore, when Q-learning species interact with fixed-mobility counterparts, those with adaptive mobility exhibit a significant evolutionary advantage. Our study suggests that reinforcement learning may offer a promising new perspective for uncovering the mechanisms of biodiversity and informing conservation strategies.