Every day, we are reminded of the state of our environment by news stories about harsh weather, melting ice caps, and endangered animals. The profound scale and intensity of these challenges may leave one to wonder, “What should we do first?”
In order to prioritize failing ecosystems by assessing their distance from tipping points, researchers recently created formulas that can aid with that topic.
A team led by Jianxi Gao, assistant professor of computer science at Rensselaer Polytechnic Institute, created equations that enable comparison of tipping point lengths across multiple mutualistic systems in research that was recently published in Nature Ecology & Evolution.
To put it another way, for the first time, different settings can be studied to see how near they are to changing entirely and, maybe, irrevocably, and they can be compared to others to see which regions require action the most urgently.
In the past, scientists were able to identify early warning signs that a system might be getting close to its tipping point, but they were unable to assign a precise number to the distance a system was from its tipping point. The value may indicate how likely it is for a system to go from the desired state to the undesirable state or how quickly a tipping point can be achieved.
To make the data in complicated systems simpler, Gao’s team created a generic dimension reduction approach, which enables precise assessments of the separations between tipping points in various ecosystems. The researchers also created a scaling factor to compare the resilience of various systems by placing them on the same scale.
“With so many ecosystems struggling from the impacts of climate change, being able to convey how little time we have left to intervene before a tipping point is reached is critical,” said Curt Breneman, Dean of the Rensselaer School of Science. “Mobilization will not happen without a sense of urgency.”
Gao’s team investigated the several variables that affect the resilience of 54 diverse habitats from around the globe. An ecosystem experiences “perturbations” due to species extinction, invasions, human activity, and environmental changes, but the structural characteristics of the ecosystem determine how likely it is to collapse.
With so many ecosystems struggling from the impacts of climate change, being able to convey how little time we have left to intervene before a tipping point is reached is critical. Mobilization will not happen without a sense of urgency.
Curt Breneman
The effect on the ecosystem, for instance, will be limited if a few trees are removed from a dense forest since other trees will grow in their place and the ecosystem will quickly return to its pre-cut form.
However, in a region with few trees, the loss of a few could have a greater effect and cause the system to change in a way that is difficult to reverse. Resilience is defined mathematically as the separation from the attraction basin’s edge.
“For example, if one piece of attraction is the forest and the other is the savannah, the system may or may not transfer to savannah because of many factors,” Gao said. “The base of attraction refers to the region of these factors inside high-dimensional space. Where is the region of forest where if you cross the boundary, it changes to savannah? If a system remains in the boundary, it will always recover. Only when it crosses some value will it change into another state and cannot recover.”
According to Gao’s team, the technique can also be used to assess the resilience of biological, engineering, and social systems in addition to ecological systems.
“The dimension reduction approach is very general and can be applied to different types of systems,” Gao said. “It’s universal.”
The team also calculated a supply chain network’s tipping point. Positively, the research team’s findings do not yet point to any tipping points. They are also working on a system failure restoration algorithm.
Gao was joined in research by Huixin Zhang and Weidong Zhang of Shanghai Jiao Tong University, Qi “Ryan” Wang of Northeastern University, and Shlomo Havlin of Bar-Ilan University. Their work was supported by the NSF CAREER Award.
