Rate-Induced Transitions in Networked Complex Adaptive Systems: Exploring Dynamics and Management Implications Across Ecological, Social, and Socioecological Systems
V\'itor V. Vasconcelos, Fl\'avia M.D. Marquitti, Theresa Ong, Lisa C., McManus, Marcus Aguiar, Amanda B. Campos, Partha S. Dutta, Kristen Jovanelly,, Victoria Junquera, Jude Kong, Elisabeth H. Krueger, Simon A. Levin, Wenying, Liao, Mingzhen Lu, Dhruv Mittal, Mercedes Pascual

TL;DR
This paper introduces a framework to understand rate-induced transitions in complex adaptive systems, highlighting how the rate of environmental change can trigger systemic shifts and informing management strategies to enhance resilience.
Contribution
It presents a novel model capturing RITs in networked CASs, revealing how different nodes respond to environmental change rates and informing adaptive management policies.
Findings
Lower-degree nodes tip first due to fewer connections.
High-degree nodes tip later as their sources collapse.
RITs occur at critical environmental change rates across network structures.
Abstract
Complex adaptive systems (CASs), from ecosystems to economies, are open systems and inherently dependent on external conditions. While a system can transition from one state to another based on the magnitude of change in external conditions, the rate of change -- irrespective of magnitude -- may also lead to system state changes due to a phenomenon known as a rate-induced transition (RIT). This study presents a novel framework that captures RITs in CASs through a local model and a network extension where each node contributes to the structural adaptability of others. Our findings reveal how RITs occur at a critical environmental change rate, with lower-degree nodes tipping first due to fewer connections and reduced adaptive capacity. High-degree nodes tip later as their adaptability sources (lower-degree nodes) collapse. This pattern persists across various network structures. Our study…
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Taxonomy
TopicsEcosystem dynamics and resilience · Complex Systems and Decision Making · Sustainability and Ecological Systems Analysis
