Investigating Mobility in Spatial Biodiversity Models through Recurrence Quantification Analysis

TL;DR

This study uses recurrence quantification analysis to explore how species mobility affects the dynamics of spatial ecological models, revealing distinct regimes and providing a method to infer mobility from observed data.

Contribution

It introduces an ensemble-based recurrence quantification approach to identify ecological regimes and infer mobility parameters in spatial biodiversity models.

Findings

Recurrence quantifiers reflect different ecological states as mobility varies.

Outliers in recurrence quantifiers correspond to specific ecological regimes.

The method enables inferring mobility parameters from numerical data.

Abstract

Recurrence plots and their associated quantifiers provide a robust framework for detecting and characterising complex patterns in non-linear time-series. In this paper, we employ recurrence quantification analysis to investigate the dynamics of the cyclic, non-hierarchical May-Leonard model, also referred to as rock--paper--scissors systems, that describes competitive interactions among three species. A crucial control parameter in these systems is the species' mobility $m$, which governs the spatial displacement of individuals and profoundly influences the resulting dynamics. By systematically varying $m$ and constructing suitable recurrence plots from numerical simulations, we explore how recurrence quantifiers reflect distinct dynamical features associated with different ecological states. We then introduce an ensemble-based approach that leverages statistical distributions of recurrence quantifiers, computed from numerous independent realisations, allowing us to identify dynamical outliers as significant deviations from typical system behaviour. Through detailed numerical analyses, we demonstrate that these outliers correspond to divergent ecological regimes associated with specific mobility values, providing also a robust manner to infer the mobility parameter from observed numerical data. Our results highlight the potential of recurrence-based methods as diagnostic tools for analysing spatial ecological systems and extracting ecologically relevant information from their non-linear dynamical patterns.