Earth is a complex and dynamic place that relies on thousands of systems to maintain balance and resiliency. Every system has a tipping point, a point where the system, be it biological, ecological, or technical collapses. The ability of a system to withstand or recover from changes is called its resiliency. A resilient system will continue to function even in the face of internal or external disruption. Too much change or disruption swings the pendulum towards a system collapse. The rapid decline of honey bee populations and the effect of that decline on plants dependent upon those pollinators is an example of a disruption veering dangerously close to collapse.
Understanding and predicting a system’s tipping point is an essential step in planning and implementing preventive measures whether it be the collapse of the pollination dependency on the world’s rapidly declining honeybee population or the failure of a major power grid.
In one example, an ecosystem in Australia is diagrammed showing the interconnectivity between plants (shown as purple icons) and the ant species that provide a protective and symbiotic relationship to those plants (shown as connecting lines). As ant species are removed from the timeline at the bottom of the diagram, new network connections are established in a resilient ecosystem. If ant species continue to be removed, the ecosystem reaches a tipping point and the number of plants species quantified on the timeline graph plummets as the system collapses.
Being able to quantify that tipping point has been a challenge to scientist. A recent paper published in Nature describes a new tool that may provide the answer. Researchers looked at complex systems to derive a mathematical model that aligned the point of all system’s collapses, thereby allowing for the prediction of other systems’ tipping points in order to understand which changes might trigger catastrophic damage. The tool can be applied to a range of networked systems in order to understand influential impacts to commercial fishery, airline traffic, and the spread of diseases.
Gao, J., Barzel, B., & Barabási, A. (2016). Universal resilience patterns in complex networks. Nature, 530(7590), 307-312. doi:10.1038/nature16948
A data visualization video was created by Mauro Martino, and Jianxi Gao entitled “Network Earth” to explain the complexities of this planet and modeling systems: