Phase transition in a dryland shrub–grass ecosystem: Emergence through local trade-offs

Ecosystems are complex adaptive systems in which local interactions among individuals shape macroscopic phenomena such as pattern formation, multistability, and critical transitions. However, how such bottom-up interactions govern emergent behaviors and system-level complexity remains a central challenge in ecology. Phase transition theory, a classic paradigm in physics, links local interaction rules to emergent changes in macroscopic system states, offering a formal language for understanding cross-scale dynamics. Here we unite this theory with empirical evidence from dryland shrub–grass ecosystems and show that these systems exhibit three hallmarks of phase transitions. First, abrupt shifts separate different phases of deserts, shrublands, and grasslands. Second, shrublands and grasslands self-organize into Turing-like phase patterns, contrasting with homogeneous deserts. Third, special phenomena emerge near-transition threshold, including patch enlargement and abrupt decline at desert–vegetation interface, and fairy circles within the shrub–grass transition zone. Using a cellular automaton model built on a fundamental trade-off between extensive and intensive resource foraging strategies (EI trade-off), we show how kernels encoding local trade-offs integrate these seemingly disparate phenomena within a unified phase transition framework. Moreover, this EI trade-off was empirically validated through analyses of plant functional traits and spatial patterns of plant patches. These findings illustrate that dryland ecosystem dynamics reflect universal self-organization principles. Our work establishes phase-transition theory as a powerful framework for understanding, predicting, and managing complexity in dryland ecosystems, which advances the integration of complexity science with ecology, offering a pathway toward bottom-up ecosystem understanding.