Modification speed and radius of higher-order interactions alter the oscillatory dynamics in an agent-based model

TL;DR

This study explores how the speed and spatial influence of higher-order interactions in an ecological agent-based model significantly affect species populations and oscillatory dynamics, revealing complex behaviors beyond traditional models.

Contribution

It introduces a novel agent-based model incorporating higher-order interactions with variable speed and radius, analyzing their effects on ecosystem dynamics.

Findings

Interaction modification speed influences oscillation patterns

Radius of influence affects species abundance and stability

Higher-order interactions induce complex dynamical behaviors

Abstract

Understanding the population dynamics of ecological systems is crucial for predicting shifts in biodiversity and ensuring the protection of these systems. Established models often focus on pairwise species interactions, yet recent studies have highlighted the importance of higher-order interactions (HOIs) in shaping community structure and function. In this study, we investigate the effects of HOIs in an agent-based model with three species engaged in intransitive competition. We introduce an HOI where one species modifies the competition between the other two. We explore the impact of the strength, radius of influence, and speed of this interaction modification on species abundances and oscillations thereof. Our results show that these abundances are not only greatly impacted by the strength, but also by the radius and speed of the interaction modification. A deeper investigation demonstrates that the changes in the oscillations are caused by the interaction modification itself, and not the change in pairwise interaction strength caused by the HOI. These results emphasize the importance of considering the spatio-temporal scales of higher-order interactions when assessing ecosystem stability, highlighting that such interactions can introduce complex dynamical behaviors that go beyond the predictions of traditional pairwise or simpler higher-order models